diff --git a/Rethinking_2/Chp_07.ipynb b/Rethinking_2/Chp_07.ipynb
index 9d75b71..94bdb4f 100644
--- a/Rethinking_2/Chp_07.ipynb
+++ b/Rethinking_2/Chp_07.ipynb
@@ -27,13 +27,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "%config Inline.figure_format = 'retina'\n",
     "az.style.use('arviz-darkgrid')\n",
-    "az.rcParams['stats.credible_interval'] = 0.89  # set credible interval for entire notebook\n",
+    "az.rcParams['stats.hdi_prob'] = 0.89  # set credible interval for entire notebook\n",
     "np.random.seed(0)"
    ]
   },
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -133,7 +133,7 @@
        "6      sapiens   1350  53.5"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -160,12 +160,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x480 with 1 Axes>"
       ]
@@ -180,7 +180,7 @@
     "plt.scatter(brains.mass, brains.brain)\n",
     "\n",
     "# point labels\n",
-    "for i, r in brains.iterrows():\n",
+    "for _, r in brains.iterrows():\n",
     "    if r.species == \"afarensis\":\n",
     "        plt.text(r.mass + 0.5, r.brain, r.species, ha=\"left\", va=\"center\")\n",
     "    elif r.species == \"sapiens\":\n",
@@ -201,10 +201,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# standardise\n",
     "brains.loc[:, \"mass_std\"] = (\n",
     "    brains.loc[:, \"mass\"] - brains.loc[:, \"mass\"].mean()\n",
     ") / brains.loc[:, \"mass\"].std()\n",
@@ -222,14 +223,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/oscar/miniconda3/envs/py3/lib/python3.7/site-packages/statsmodels/stats/stattools.py:71: ValueWarning: omni_normtest is not valid with less than 8 observations; 7 samples were given.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\statsmodels\\stats\\stattools.py:71: ValueWarning: omni_normtest is not valid with less than 8 observations; 7 samples were given.\n",
       "  \"samples were given.\" % int(n), ValueWarning)\n"
      ]
     },
@@ -248,10 +249,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   4.807</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Tue, 19 May 2020</td> <th>  Prob (F-statistic):</th>  <td>0.0798</td> \n",
+       "  <th>Date:</th>             <td>Fri, 25 Sep 2020</td> <th>  Prob (F-statistic):</th>  <td>0.0798</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>16:36:51</td>     <th>  Log-Likelihood:    </th> <td>  2.9925</td>\n",
+       "  <th>Time:</th>                 <td>14:02:23</td>     <th>  Log-Likelihood:    </th> <td>  2.9925</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>     7</td>      <th>  AIC:               </th> <td>  -1.985</td>\n",
@@ -300,8 +301,8 @@
        "Dep. Variable:              brain_std   R-squared:                       0.490\n",
        "Model:                            OLS   Adj. R-squared:                  0.388\n",
        "Method:                 Least Squares   F-statistic:                     4.807\n",
-       "Date:                Tue, 19 May 2020   Prob (F-statistic):             0.0798\n",
-       "Time:                        16:36:51   Log-Likelihood:                 2.9925\n",
+       "Date:                Fri, 25 Sep 2020   Prob (F-statistic):             0.0798\n",
+       "Time:                        14:02:23   Log-Likelihood:                 2.9925\n",
        "No. Observations:                   7   AIC:                            -1.985\n",
        "Df Residuals:                       5   BIC:                            -2.093\n",
        "Df Model:                           1                                         \n",
@@ -323,7 +324,7 @@
        "\"\"\""
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -342,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -376,8 +377,8 @@
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "      <th>sd</th>\n",
-       "      <th>hpd_5.5%</th>\n",
-       "      <th>hpd_94.5%</th>\n",
+       "      <th>hdi_5.5%</th>\n",
+       "      <th>hdi_94.5%</th>\n",
        "      <th>mcse_mean</th>\n",
        "      <th>mcse_sd</th>\n",
        "      <th>ess_mean</th>\n",
@@ -421,7 +422,7 @@
        "</div>"
       ],
       "text/plain": [
-       "    mean     sd  hpd_5.5%  hpd_94.5%  mcse_mean  mcse_sd  ess_mean  ess_sd  \\\n",
+       "    mean     sd  hdi_5.5%  hdi_94.5%  mcse_mean  mcse_sd  ess_mean  ess_sd  \\\n",
        "b  0.169  0.074     0.058      0.294      0.002    0.002    1021.0   956.0   \n",
        "a  0.528  0.069     0.410      0.634      0.002    0.002     867.0   798.0   \n",
        "\n",
@@ -430,7 +431,7 @@
        "a     882.0     983.0    NaN  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -452,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -461,7 +462,7 @@
        "0.49015804794908413"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -479,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -488,7 +489,7 @@
        "0.49015804794908413"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -510,14 +511,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/oscar/miniconda3/envs/py3/lib/python3.7/site-packages/statsmodels/stats/stattools.py:71: ValueWarning: omni_normtest is not valid with less than 8 observations; 7 samples were given.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\statsmodels\\stats\\stattools.py:71: ValueWarning: omni_normtest is not valid with less than 8 observations; 7 samples were given.\n",
       "  \"samples were given.\" % int(n), ValueWarning)\n"
      ]
     },
@@ -536,10 +537,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2.310</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Tue, 19 May 2020</td> <th>  Prob (F-statistic):</th>  <td> 0.215</td> \n",
+       "  <th>Date:</th>             <td>Fri, 25 Sep 2020</td> <th>  Prob (F-statistic):</th>  <td> 0.215</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>16:36:52</td>     <th>  Log-Likelihood:    </th> <td>  3.3223</td>\n",
+       "  <th>Time:</th>                 <td>14:03:13</td>     <th>  Log-Likelihood:    </th> <td>  3.3223</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>     7</td>      <th>  AIC:               </th> <td> -0.6445</td>\n",
@@ -591,8 +592,8 @@
        "Dep. Variable:              brain_std   R-squared:                       0.536\n",
        "Model:                            OLS   Adj. R-squared:                  0.304\n",
        "Method:                 Least Squares   F-statistic:                     2.310\n",
-       "Date:                Tue, 19 May 2020   Prob (F-statistic):              0.215\n",
-       "Time:                        16:36:52   Log-Likelihood:                 3.3223\n",
+       "Date:                Fri, 25 Sep 2020   Prob (F-statistic):              0.215\n",
+       "Time:                        14:03:13   Log-Likelihood:                 3.3223\n",
        "No. Observations:                   7   AIC:                           -0.6445\n",
        "Df Residuals:                       4   BIC:                           -0.8068\n",
        "Df Model:                           2                                         \n",
@@ -615,7 +616,7 @@
        "\"\"\""
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -634,7 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -660,7 +661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -683,12 +684,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:33: UserWarning: This figure was using constrained_layout==True, but that is incompatible with subplots_adjust and or tight_layout: setting constrained_layout==False. \n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 600x850 with 6 Axes>"
       ]
@@ -742,7 +750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -751,12 +759,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 800x300 with 2 Axes>"
       ]
@@ -822,7 +830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -831,7 +839,7 @@
        "0.6108643020548935"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -843,12 +851,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x480 with 1 Axes>"
       ]
@@ -879,7 +887,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -896,7 +904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -906,7 +914,7 @@
        "        0.44851465, -0.8559313 ])"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -936,7 +944,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -970,16 +978,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 2.54799078,  2.3115561 ,  2.89843278,  3.53621752, 11.04367575])"
+       "array([ 2.5371182 ,  2.35026461,  2.81184387,  3.49461945, 11.01862116])"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1004,7 +1012,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1061,8 +1069,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
+   "execution_count": 31,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1077,314 +1087,4357 @@
      "text": [
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:02<00:00, 986.17draws/s] \n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:02<00:00, 940.71draws/s] \n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:02<00:00, 805.99draws/s] \n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1148.87draws/s]\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:02<00:00, 732.77draws/s] \n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1132.31draws/s]\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1097.05draws/s]\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:02<00:00, 856.80draws/s]\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1117.58draws/s]\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:02<00:00, 968.67draws/s] \n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n"
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3\n"
-     ]
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:18<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
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     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
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-      "Initializing NUTS using jitter+adapt_diag...\n",
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-      "Initializing NUTS using jitter+adapt_diag...\n",
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-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
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-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
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-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
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-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:02<00:00, 949.01draws/s] \n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:02<00:00, 948.06draws/s] \n",
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 62 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1143.00draws/s]\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:02<00:00, 876.46draws/s] \n"
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4\n"
-     ]
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+       "      100.00% [8000/8000 00:16<00:00 Sampling 4 chains, 0 divergences]\n",
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+       "    "
+      ],
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+       "<IPython.core.display.HTML object>"
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     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 64 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
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-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1053.62draws/s]\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1018.15draws/s]\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
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-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
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-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1153.38draws/s]\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:02<00:00, 983.81draws/s] \n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:02<00:00, 866.76draws/s]\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1122.08draws/s]\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1047.47draws/s]\n"
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
      ]
     },
     {
-     "name": "stdout",
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
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+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:16<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
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+    {
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "5\n"
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 65 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
      ]
     },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
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+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
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+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:18<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 64 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
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-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
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-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1073.69draws/s]\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1091.23draws/s]\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
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-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
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-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:02<00:00, 955.24draws/s] \n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1030.75draws/s]\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1087.71draws/s]\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1025.17draws/s]\n"
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
      ]
     },
     {
-     "name": "stdout",
+     "data": {
+      "text/html": [
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+       "      100.00% [8000/8000 00:20<00:00 Sampling 4 chains, 0 divergences]\n",
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+       "    "
+      ],
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+       "<IPython.core.display.HTML object>"
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      "output_type": "stream",
      "text": [
-      "6\n"
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 62 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
      ]
     },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
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+       "                border: none;\n",
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+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:24<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 66 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:02<00:00, 962.10draws/s] \n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1044.93draws/s]\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:02<00:00, 893.47draws/s] \n",
-      "Auto-assigning NUTS sampler...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
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+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:19<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
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+     "metadata": {},
+     "output_type": "display_data"
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+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 63 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:17<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 59 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:19<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 64 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:19<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 68 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:17<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 65 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:17<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 67 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:19<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 66 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:19<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 64 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:14<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 65 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:18<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 63 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:14<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 61 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:19<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 66 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:17<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 63 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:15<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 62 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:18<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 66 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:18<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 65 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:18<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 66 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:17<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 65 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:17<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 69 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:18<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 68 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:17<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 63 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:26<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 77 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:18<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 65 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:19<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 71 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:16<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 65 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:16<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 61 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:18<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 61 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:18<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 63 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:18<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 67 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:18<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 65 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:16<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 65 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:17<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 62 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:19<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 67 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:22<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 73 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:16<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 67 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:17<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 72 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:14<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 71 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:16<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 73 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:22<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 78 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:17<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 71 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:15<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 66 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:17<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 66 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:16<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 67 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:16<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 69 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n"
+     ]
+    }
+   ],
+   "source": [
+    "n = 20\n",
+    "tries = 10\n",
+    "param = 6\n",
+    "r = np.zeros(shape=(param - 1, 4))\n",
+    "\n",
+    "train = []\n",
+    "test = []\n",
+    "\n",
+    "for j in range(2, param + 1):\n",
+    "    print(j)\n",
+    "    for i in range(1, tries + 1):\n",
+    "        tr, te = sim_train_test(N=n, k=param)\n",
+    "        train.append(tr), test.append(te)\n",
+    "    r[j - 2, :] = (\n",
+    "        np.mean(train),\n",
+    "        np.std(train, ddof=1),\n",
+    "        np.mean(test),\n",
+    "        np.std(test, ddof=1),\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:47<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 110 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:49<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 99 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:49<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 99 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:51<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 102 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:50<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 97 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:50<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 98 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:48<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 97 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:46<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 96 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:45<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 95 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:48<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 98 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:49<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 99 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:46<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 95 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:48<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 98 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:48<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 95 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:51<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 100 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:51<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 102 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:52<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 104 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:47<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 97 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:54<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 103 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:47<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 96 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:51<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 100 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:49<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 97 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:49<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 96 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:47<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 98 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:50<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 97 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:44<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 94 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:47<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 98 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:52<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 101 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:46<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 96 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:47<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 96 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:44<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 92 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:43<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 93 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:49<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 97 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:49<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 95 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:50<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 99 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:46<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 92 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:52<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 96 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1080.25draws/s]\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:46<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 89 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1123.38draws/s]\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:53<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 106 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1002.28draws/s]\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:52<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 105 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1136.63draws/s]\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:50<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 98 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1064.63draws/s]\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:50<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 102 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1045.91draws/s]\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:50<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 100 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [vec_V]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1141.00draws/s]\n"
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:53<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 102 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:53<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 97 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:50<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 97 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:47<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 96 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:56<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 108 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:50<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 100 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [vec_V]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:46<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 94 seconds.\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n"
      ]
     }
    ],
    "source": [
-    "n = 20\n",
+    "n = 100\n",
     "tries = 10\n",
     "param = 6\n",
-    "r = np.zeros(shape=(param - 1, 4))\n",
+    "r_100 = np.zeros(shape=(param - 1, 4))\n",
     "\n",
     "train = []\n",
     "test = []\n",
@@ -1394,7 +5447,7 @@
     "    for i in range(1, tries + 1):\n",
     "        tr, te = sim_train_test(N=n, k=param)\n",
     "        train.append(tr), test.append(te)\n",
-    "    r[j - 2, :] = (\n",
+    "    r_100[j - 2, :] = (\n",
     "        np.mean(train),\n",
     "        np.std(train, ddof=1),\n",
     "        np.mean(test),\n",
@@ -1420,12 +5473,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1481,6 +5534,117 @@
     "These uncertainties are a *lot* larger than in the book... MCMC vs OLS again?"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_param = np.arange(2, param + 1)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(num_param, r_100100[:, 0], color=\"C0\")\n",
+    "plt.xticks(num_param)\n",
+    "\n",
+    "for j in range(param - 1):\n",
+    "    plt.vlines(\n",
+    "        num_param[j],\n",
+    "        r[j, 0] - r[j, 1],\n",
+    "        r[j, 0] + r[j, 1],\n",
+    "        color=\"mediumblue\",\n",
+    "        zorder=-1,\n",
+    "        alpha=0.80,\n",
+    "    )\n",
+    "\n",
+    "plt.scatter(num_param + 0.1, r_100[:, 2], facecolors=\"none\", edgecolors=\"k\")\n",
+    "\n",
+    "for j in range(param - 1):\n",
+    "    plt.vlines(\n",
+    "        num_param[j] + 0.1,\n",
+    "        r_100[j, 2] - r_100[j, 3],\n",
+    "        r_100[j, 2] + r_100[j, 3],\n",
+    "        color=\"k\",\n",
+    "        zorder=-2,\n",
+    "        alpha=0.70,\n",
+    "    )\n",
+    "\n",
+    "dist = 0.20\n",
+    "plt.text(num_param[1] - dist, r_100[1, 0] - dist, \"in\", color=\"C0\", fontsize=13)\n",
+    "plt.text(num_param[1] + dist, r_100[1, 2] - dist, \"out\", color=\"k\", fontsize=13)\n",
+    "plt.text(\n",
+    "    num_param[1] + dist,\n",
+    "    r_100[1, 2] + r_100[1, 3] - dist,\n",
+    "    \"+1 SD\",\n",
+    "    color=\"k\",\n",
+    "    fontsize=10,\n",
+    ")\n",
+    "plt.text(\n",
+    "    num_param[1] + dist,\n",
+    "    r_100[1, 2] - r_100[1, 3] - dist,\n",
+    "    \"+1 SD\",\n",
+    "    color=\"k\",\n",
+    "    fontsize=10,\n",
+    ")\n",
+    "plt.xlabel(\"Number of parameters\", fontsize=14)\n",
+    "plt.ylabel(\"Deviance\", fontsize=14)\n",
+    "plt.title(\"N = {}\".format(n), fontsize=14)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Fig 7.7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Density')"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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rANNeesfUsK/1g/1cOocOHcLw4cPlj6tWrYp///23yOcU1dc///wzPvvsM/ljf39/HD58WDvBWhi+p/WHfa0/ptzXXPqPiIhKTHnkWZMSEnXPZxkJEVkSJttERKRAmyuRqHs+y0iIyJIw2SYiIgXaWmO7sOdLJBLuWUBEFoPJNhERKdDW7pFFPZ+lJERkKZhsExGRAm2PbDs5OcHW1lahjaUkRGQpmGwTEZECbSfb3LKdiCwZk20iIlKg7TISddfgyDYRWQom20REJJednY2MjAyFNk1HttVdgyPbRGQpmGwTEZHc8+fPVdq0MbLNZJuILBWTbSIiklMu77C1tYWzs7PG12UZCRFZKibbREQkpzziXL58eYhEIo2vy5FtIrJUTLaJiEhOF5MjASbbRGS5mGwTEZGctpf9y8cyEiKyVEy2iYhITjkJ1layrXyd9PR0vH79WivXJiIyZky2iYhITldlJNyynYgsFZNtIiKS01UZiYuLC6ytrYu8FxGROWKyTUREcroa2baysuIkSSKySEy2iYhITlcj2+quxUmSRGQJmGwTEREAICcnBy9evFBo02WyzZFtIrIETLaJiAiA+uRXW2Uk6q7FkW0isgRMtomICIBqsl2mTBm4urpq7foc2SYiS8Rkm4iIAKiv17ay0t6PCSbbRGSJmGwTEREA3W1ok49lJERkiZhsExERAN2uRKLuehzZJiJLwGSbiIgA6G6N7cKul5qaCqlUqtV7EBEZGybbREQEQP8j2wDw/Plzrd6DiMjYMNkmIiIAuk+23dzcVCZcspSEiMwdk20iIgKg+zKSMmXKoHz58kXek4jI3DDZJiIiALof2VZ3TY5sE5G5Y7JNRETIzc1FamqqQpu2R7YBJttEZHmYbBMREZ4/fw5BEBTadDGyzbW2icjSMNkmIiKVEWYrKyu4u7tr/T4c2SYiS8Nkm4iIVEaY3d3dUaZMGa3fRznZ5sg2EZk7JttERKSXyZGAahkJR7aJyNxZGzqAwuzfvx8xMTG4du0a4uLiIJVKsXDhQvTr16/Y14iKisKIESMKPb59+3Y0bdpUpf3KlSsICwvD5cuXIZVK4eXlhZCQEPTq1as0L4WIyOgpjzDrKtlmGQkRWRqjTba///57JCcnw9XVFRUqVEBycnKpr+Xn5wc/Pz+V9ooVK6q0RUVFYcyYMbCxsUHPnj3h6OiI8PBwzJgxA8nJyRg3blyp4yAiMlbKSa8uViJRd92UlBTk5eXppGSFiMgYGG2yPX/+fFSvXh2enp5Yt24dli1bVupr+fn5YdKkSW89Lzc3F1988QVEIhE2b96MBg0aAAAmTJiAIUOGICwsDN26dUONGjVKHQsRkTHSVxmJ8nVlMhlSUlJ0ltwTERma0dZst2rVCp6ennq9Z2RkJO7fv4/g4GB5og0AYrEY48ePR25uLvbs2aPXmIiI9EHXu0fmK1++PEQikUIbS0mIyJwZ7ci2Nt27dw8bNmxAdnY2KleujFatWsHNzU3lvOjoaABAUFCQyrHWrVsrnENEZE70NbJtbW0NNzc3pKSkyNuePn2Khg0b6uR+RESGZhHJ9oEDB3DgwAH5Y3t7e0yaNAmhoaEK5927dw8AUL16dZVrODs7w9XVFYmJiTqNlYjIEPSVbOdfu2CyzZFtIjJnZp1su7m5YebMmWjfvj0qV66MjIwMREVFYenSpViyZAnEYjGGDBkiP18ikQAAHB0d1V5PLBbj8ePHRd7T2dkZVlZGW53zVq6uroYOwWKwr/WD/fx2eXl5eP78uUKbl5dXifuuuOdXrlwZN2/elD+WSCT8dyoB9pX+sK/1x5z72qyT7Tp16qBOnTryx2XLlkXv3r1Rr1499OvXD2FhYRg0aJBWk+P09HStXUvfXF1dkZaWZugwLAL7Wj/Yz8Xz7NkzyGQyhTY7O7sS9V1J+lr5h2piYiL/nYqJ72n9YV/rjyn3dXG+JJjuEKwGvL290aRJEzx//lyhLEQsFgMAMjMz1T5PIpEUOupNRGSq1JVxlC9fXmf341rbRGRJLDLZBv7/m0h2dra8LX9JP3V12enp6UhLS1Nbz01EZMqUVyJxc3ODjY2Nzu6nvNIJt2wnInNmkcl2bm4url+/DpFIhEqVKsnbW7ZsCQA4e/asynMiIiIAQO3mOEREpkyfkyPVXZ8j20Rkzswi2U5NTUV8fDxSU1MV2i9dugRBEBTacnNzsXjxYiQnJyMoKAguLi7yY4GBgahatSoOHDiAGzduyNslEgnWrFkDa2tr9O3bV6evhYhI3/S1xnZh12eyTUTmzGgnSO7cuRMxMTEAgLi4OHlb/jrXnTp1QqdOnQAAmzdvxurVqzFx4kSFnSKnT58OAGjWrBneeecdZGZm4sKFC0hISEDlypUxd+5chXtaW1tj/vz5CA0NxdChQxEcHAyxWIzw8HAkJSVhypQpqFmzps5fOxGRPhnDyLZMJjPplZyIiApjtMl2TEwM9u7dq9AWGxuL2NhYAICnp6c82S7MkCFDcObMGURHRyMtLQ3W1taoVq0axo0bh9GjR8PZ2VnlOQEBAdiyZQtWrVqFw4cPQyqVwsvLC5MnT0bv3r219wKJiIyEoZPtvLw8pKWlwd3dXaf3JSIyBJGgXGdBGjHVpWsA0156x9Swr/WD/Vw8AwcOxN9//y1//OWXX2Lq1KklukZJ+jonJwcVK1ZUaIuIiED9+vVLdE9LxPe0/rCv9ceU+5pL/xER0Vvpe2Tb1tZWYb6MuhiIiMwFk20iIgunnOjqeoIkwBVJiMhyMNkmIrJggiDofWQb4FrbRGQ5mGwTEVmwjIwM5ObmKrTpI9lW3qFSeelWIiJzwWSbiMiCqUtyizPhR1Nubm5vjYOIyBww2SYismApKSkKj+3t7eHg4KDz+yon28pxEBGZCybbREQWTHlE2c3NDSKRSOf3VV5TmyPbRGSumGwTEVkw5RFlfW0so3wfjmwTkblisk1EZMGUk1zl8g5dYc02EVkKJttERBZMOck15Mg2NzQmInPEZJuIyIIZSxlJbm4uMjMz9XJvIiJ9YrJNRGTB0tLSFB7rY9m/wu7DUhIiMkdMtomILJihRrbLlSsHOzu7ImMhIjIHTLaJiCyYoSZIikQiTpIkIovAZJuIyIIZaoKkuntxZJuIzBGTbSIiCyWTyZhsExHpGJNtIiILlZ6eDplMptCmrzISdfdiGQkRmSMm20REFkrdSLI+k22ObBORJWCyTURkoZRHkh0cHFC2bFm93V95+T+ObBOROWKyTURkoZSTW32OagOqI9tMtonIHDHZJiKyUIZaY7uw+7GMhIjMEZNtIiILZag1tgu7H0e2icgcMdkmIrJQhlz2T939UlNTVVZHISIydUy2iYgslLGNbOfl5SEjI0OvMRAR6RqTbSIiC2XokW11yT3rtonI3DDZJiKyUIZejUTdUoOs2yYic8Nkm4jIQhk62VZ3TybbRGRumGwTEVkoQy/9p+6eLCMhInPDZJuIyALl5eUhLS1Noc0QybbyyDaTbSIyN0y2iYgs0IsXLyAIgkKbIcpIuIskEZk7JttERBZI3QiyMSTbHNkmInPDZJuIyAIpjyCLxWLY2dnpPQ5OkCQic8dkm4jIAhnDSiTq7stkm4jMDZNtIiILZAwrkQCcIElE5s/a0AEUZv/+/YiJicG1a9cQFxcHqVSKhQsXol+/fsW+xsWLF3H8+HFER0cjOTkZWVlZ8PT0xLvvvouxY8fCyclJ5TkdO3ZEcnKy2usNHjwY8+bNK/VrIiIyFsYyss0JkkRk7ow22f7++++RnJwMV1dXVKhQodAEuCiTJ09GWloaWrRogffeew8ikQjR0dH45ZdfEB4ejm3btqkdzXF0dERISIhKu4+PT6leCxGRsTGWkW3l+6alpSEvLw9lypQxSDxERNpmtMn2/PnzUb16dXh6emLdunVYtmxZia8REhKCPn36oEKFCvI2QRAwd+5cbN26FatXr8ZXX32l8jwnJydMmjRJo/iJiIyZcrJtLDXbMpkM6enpBouHiEjbjLZmu1WrVvD09NToGh999JFCog0AIpEI48ePBwBcuHBBo+sTEZkq5XINY6nZBli3TUTmxWhHtnXJ2vrNyy7s15Q5OTnYu3cvnjx5AicnJzRv3hz16tXTZ4hERDplLGUk9vb2EIvFkEgk8raUlBTUqVPHIPEQEWmbRSbbu3fvBgC0bt1a7fFnz55h1qxZCm1t2rTB4sWL+atNIjILylu1u7q6GiiSN/cumGwrx0ZEZMosLtm+ceMGfvjhB7i7uyM0NFTleL9+/eDn5wcvLy/Y2toiPj4eq1evxunTpzF+/Hhs3boVIpGo0Os7OzvDyspoq3PeypA/cC0N+1o/2M/qKZeR1KhRQ+O+Ku3zK1SogAcPHsgfZ2dn89+tCOwb/WFf648597VFJdsPHjzA2LFjkZeXh+XLl6sdpZ44caLC4yZNmmDt2rUYPnw4YmJicOrUKbRv377Qe6Snp2s7bL1xdXXliJKesK/1g/2sXm5urkq/2NjYaNRXmvS1s7OzwuMHDx7w360QfE/rD/taf0y5r4vzJcF0h2BLKDk5GSEhIUhNTcWqVasQEBBQ7OdaWVnJ1/eOjY3VVYhERHqh7oeaoWq21d2bEySJyJxYRLKdlJSEDz74AE+fPsXKlSvRoUOHEl8j/5vLq1evtB0eEZFeqUtmDTkfhbtIEpE5M/tkOykpCSNGjMDTp0+xYsUKdOrUqVTXuXLlCgBovBwhEZGhKddrOzk5wcbGxkDRcBdJIjJvZpFsp6amIj4+XuUDOj/RfvLkCZYvX47OnTsXeZ07d+4gIyNDpf3ixYtYv349bG1t0aVLF63GTkSkb8ay7F9h9+fINhGZE6OdILlz507ExMQAAOLi4uRt0dHRAIBOnTrJR6k3b96M1atXY+LEiQo7P44YMQLJyclo2rQpbt26hVu3bqncp+D5hw8fxi+//ILAwEB4enrC1tYWcXFxiIiIgJWVFebOnYvKlSvr7DUTEemD8sCEoVcBUL6/qU6UIiJSx2iT7ZiYGOzdu1ehLTY2Vj5B0dPT860lIcnJyQCAy5cv4/Lly2rPKZhs+/v7Iz4+HtevX0d0dDRycnLg7u6OHj16YOTIkWjcuLEGr4iIyDgYy+6Rhd2fI9tEZE6MNtletGgRFi1aVKxzJ02apJA051M3kl0UPz8/+Pn5leg5RESmxtjLSF68eIHc3Fz5br9ERKbMLGq2iYio+JRHtg29M67y/QVBwIsXLwwTDBGRljHZJiKyMMY2sq0u2WcpCRGZCybbREQWRjmRNfTItq2tLRwdHRXauPwfEZkLJttERBZGebUPQ49sq4uByTYRmQsm20REFsbYRrbVxcAyEiIyF0y2iYgsiFQqVdm8yxiTbY5sE5G5YLJNRGRB1CWxxlhGwpFtIjIXGiXb+RvMEBGRaVCXxBp6B0mAI9tEZL40SraHDh2K4OBg/PHHH1wTlYjIBCgnsS4uLkaxeQxHtonIXGmUbLds2RLx8fFYtGgR2rZti+nTpyMqKkpbsRERkZYZ2xrb+ThBkojMlUbJ9saNG3H06FGMGTMGTk5OOHjwIEaOHImuXbvi559/xvPnz7UVJxERaYHysn/GMDkSUE36leMkIjJVGk+QrFatGmbMmIFTp04hLCwMQUFBePDgAZYtW4b27dvjk08+wenTpyEIgjbiJSIiDRjjsn8AR7aJyHxprVCvTJky6Ny5Mzp37ozHjx9j165d2LNnD44dO4Zjx46hYsWKGDBgAAYNGgQPDw9t3ZaIiErAVJLt9PR0SKVS2NjYGCgiIiLt0MnSfxUrVkTz5s3h4+MDQRAgCAIePXqEsLAwvPvuu/juu+8glUp1cWsiIiqC8gRJY6nZVhcHS0mIyBxodQr606dPsXv3buzevRvJyckQBAH16tXD4MGDERgYiCNHjmDTpk34/fffkZeXh88//1ybtyciorcw1gmS6pYfTElJQYUKFQwQDRGR9micbMtkMvzzzz/YuXMnzpw5g9zcXNjb26NPnz4YMmQImjRpIj933LhxGD58OPr06YODBw8y2SYi0jPlkW1jKSOxsbGBs7Mz0tPT5W1ca5uIzIFGyfbKlSuxZ88ePHv2DIIgwMvLC4MGDULfvn3h6Oio9jlisRi+vr7Yv3+/JrcmIqJSMNaRbeBNLAWTbU6SJCJzoFGy/dNPP8HW1hY9e/bEkCFD4OvrW6zn+fn5QSQSaXJrIiIqBWMd2QbexHL37l35Y45sE5E50CjZ/vTTT9GvX78Sb/Xbr18/9OvXT5NbExFRCb1+/RoSiUShzdiS7YKYbBOROdBoNZLGjRsXa5v2e/fu4cKFC5rcioiINKQueTW2MpKCWEZCROZAo2R7xIgR+Pnnn9963i+//IIRI0ZocisiItKQcrItEong4uJimGDU4Mg2EZkjjZLt/DW0i3MeEREZlvJIsaurK8qUKWOgaFRxZJuIzJFONrVR9vTpUzg4OOjjVkREVAhjXokE4Mg2EZmnEk+Q3Ldvn8Lj+/fvq7Tly83NRUJCAs6fP6+w3jYREemfMa9EAnBkm4jMU4mT7VmzZsmX7ROJRIiNjUVsbGyh5wuCADs7O0yYMKH0URIRkcaMdav2fMrxcGSbiMxBiZPtCRMmQCQSQRAE/PDDD6hfvz7effddtefa2NigQoUKaN26NbfcJSIyMHU128ZEOZ7MzEzk5OTA1tbWQBEREWmuxMn2pEmT5H/fu3cvAgMDMXHiRK0GRURE2mdqI9vAm5grVqxogGiIiLRDo01tTpw4oa04iIhIx5RHto2tZtvFxUX+m9N8TLaJyNTpZTUSIiIyPGMf2ba2tlZZ95uTJInI1JVoZHv27NkQiUSYNm0aypcvj9mzZxf7uSKRCAsWLChxgEREpB3Pnz9XeGxsI9vAmy8AaWlp8sfKMRMRmZoSJdt79+6FSCTChx9+iPLly2Pv3r3Ffi6TbSIiwxEEQWWUuHz58gaKpnDu7u64c+eO/DFHtonI1JUo2d6wYQMAoHLlygqPiYjIuL18+RLZ2dkKbR4eHgaKpnDKMXFkm4hMXYmSbT8/vyIfExGRcVKXtBpbzTagGhOTbSIydZwgSURkAZSTVjs7O4jFYgNFUzjl0hYm20Rk6jRa+u/Vq1dIS0uDi4sLHBwc5O2ZmZlYt24d4uLiULlyZYwePRpVq1Yt0bX379+PmJgYXLt2DXFxcZBKpVi4cCH69etXouvIZDJs2bIF27dvR2JiIhwcHODv74+pU6eiRo0aap9z5coVhIWF4fLly5BKpfDy8kJISAh69epVonsTERkL5aS1fPny8t2AjQmTbSIyNxol2z/++CN+/vln7NixA40aNQIA5OTkYPDgwUhISJCvlRoeHo79+/eXaDLO999/j+TkZLi6uqJChQpITk4uVYxfffUVduzYAS8vLwwfPhwpKSk4dOgQIiIisG3bNnh5eSmcHxUVhTFjxsDGxgY9e/aEo6MjwsPDMWPGDCQnJ2PcuHGlioOIyJDUJdvGiMk2EZkbjcpIzp8/jypVqsgTbQA4cOAA7t69C39/f/z6668ICQlBSkoKfv/99xJde/78+Thx4gQiIyMxZMiQUsUXGRmJHTt2wNfXF3v37sXMmTPx3XffYd26dZBIJPj6668Vzs/NzcUXX3wBkUiEzZs3Y/78+fjss8+wf/9+1KlTB2FhYbh3716pYiEiMiRTWIkEUI2Lq5EQkanTKNl+9OiRSinG8ePHYWVlhUWLFqF169aYPXs2atasiVOnTpXo2q1atYKnp6cm4WHnzp0AgClTpsDW1lbeHhgYiKCgIFy4cAEJCQny9sjISNy/fx/BwcFo0KCBvF0sFmP8+PHIzc3Fnj17NIqJiMgQnj17pvDYVJLt1NRU5OXlGSgaIiLNaZRsp6enw8nJSaHt0qVL8Pb2Vthet27dunj06JEmtyqVqKgoODg4oHnz5irHgoKCAAAXLlyQt0VHRyscK6h169YK5xARmRLlEWJjXIkEUI1LEASVnS+JiEyJRjXbHh4eePr0qfzx7du3kZaWhuDgYIXzDDEJJysrC8+ePYO3tzfKlCmjcjx/RL5gWUj+36tXr65yvrOzM1xdXZGYmFjkfZ2dnWFlZbqLvLi6uho6BIvBvtYP9vMbL168UHhcrVo1rfeNNq6nboWUnJwc/jsWwL7QH/a1/phzX2uUbNevXx+nTp3CjRs3UL9+ffz+++8QiUTo0KGDwnmJiYmoUKGCRoGWVGZmJgD1H9wF2yUSibwt/++Ojo6FPufx48dF3jc9Pb3EsRoLV1dXhW2SSXfY1/rBfv5/yp9dDg4OWu0bbfa1i4uLwpeDu3fvalxWaC74ntYf9rX+mHJfF+dLgkZDsB999BFkMhn69+8Pf39/7N69G/Xq1UNAQID8nJSUFNy8eRMNGzbU5FZERKQB5VU9jHH3yHzcRZKIzIlGyXaTJk2wZs0atGjRAuXLl0fv3r3x448/KpRR/PXXXyhXrhzatGmjcbAlkT86XXDkuqD89oIj3/l/zx8VV/ecwka9iYiMlSAIKgmrsdZsA9xFkojMi0ZlJADQvn17tG/fvtDjI0eOxMiRIzW9TYk5ODjAw8MDSUlJyMvLU6nbzq/PLriaSv7fExMT4ePjo3B+eno60tLS0KxZM12GTUSkdRKJBK9fv1ZoM9bVSACutU1E5sV0Z/IVg5+fH7KyshAbG6ty7OzZswCAli1bytvy/55/rKCIiAj5NYmITIm6ZJXJNhGRfmgt2c7NzcXz58/x8OHDQv/oSmpqKuLj41WWhxo0aBAAYOXKlcjJyZG3nz9/HmfPnkXLli1Rs2ZNeXtgYCCqVq2KAwcO4MaNG/J2iUSCNWvWwNraGn379tXZ6yAi0gXlZNXe3h7lypUzUDRvxzISIjInGpeRnDt3Dj/++CMuX76M3NzcQs8TiUS4fv16sa+7c+dOxMTEAADi4uLkbfnrXHfq1AmdOnUCAGzevBmrV6/GxIkTMWnSJPk1AgICMHDgQOzcuRN9+/ZFu3bt5Nu1i8VilR0kra2tMX/+fISGhmLo0KEIDg6GWCxGeHg4kpKSMGXKFIXknIjIFKjbPdIQS7IWl/IESe4iSUSmTKNk++TJk5g4cSLy8vLg7OyMKlWqwMHBQSuBxcTEYO/evQptsbGx8pIQT09PebJdlHnz5qFu3brYvn07Nm7cCAcHB3To0AFTp05VmzgHBARgy5YtWLVqFQ4fPgypVAovLy9MnjwZvXv31sprIyLSJ1PZPTKf8si2cvxERKZEo2R79erVkMlk+PzzzzFs2DC1m8eU1qJFi7Bo0aJinTtp0iSFEe2CrKys8MEHH+CDDz4o9r0bN26MX375pdjnExEZM1PZPTKf8pcBjmwTkSnTqGb7zp07aNq0KUaMGKHVRJuIiLRHeWTYmNfYBlTjS01NLbJMkYjImGmUbDs4OBj9ryOJiCydqY1sq4tPeQI8EZGp0CjZbtWqFf777z/IZDJtxUNERFpmSrtHAuqTba5IQkSmSqNke8aMGXj9+jUWLVqEvLw8bcVERERaZEq7RwJvVoZydXVVaGOyTUSmSqMJkrt370bbtm2xceNG/PPPP/Dz80PFihXVnisSiTBhwgRNbkdERKWgnKiaQvlf+fLlkZaWJn/MZJuITJXGq5GIRCIIgoD79+/j/v37hZ7LZJuISP8EQTDZZPv27dvyx0y2ichUaZRsL1y4UFtxEBGRDmRmZirsoAuYRrLNXSSJyFxolGxz63IiIuOmbo1qU0i2uYskEZkLjSZIEhGRcVNeY7ts2bIoV66cgaIpPu4iSUTmQqOR7Xy5ubn4559/cPXqVaSlpaFx48YYMGAAAODJkydIS0uDl5cXrK21cjsiIiomU1tjOx93kSQic6Fx9hsdHY2ZM2fiyZMnEAQBIpEIUqlUnmyfP38es2fPxsqVK9G1a1eNAyYiouIztd0j8ykn26zZJiJTpVEZya1bt/DRRx8hJSUFI0aMwPfffw9BEBTO6dq1K+zt7REeHq5RoEREVHLmMrLNZJuITJVGI9s//PADcnJy8OuvvyIwMFDtOWXLlkXt2rVx/fp1TW5FRESlYC4j22lpaZBKpbCxsTFQREREpaPRyPaFCxfQpEmTQhPtfJUrV8bTp081uRUREZWCuYxsA0BqaqoBIiEi0oxGybZEIsE777zz1vNycnIgk8k0uRUREZWCKW5oAwBubm4qbZwkSUSmSKNk28PDA/Hx8W89Ly4uDpUrV9bkVkREVAqmmmxbW1urJNxc/o+ITJFGyXZQUBDu3LmDY8eOFXrOnj178PDhQ7Rr106TWxERUSmYarINcBdJIjIPGiXbY8eOhYODA6ZNm4aVK1fi6tWrAIDs7GzExcVhzZo1mDt3LlxcXDBq1CitBExERMUjCIJJJ9vKkzmZbBORKdIo2fb09MTatWvh7OyMn376CYMGDYJIJMKRI0fw3nvvYdWqVShXrhzWrFljMjPgiYjMRWZmJqRSqUKbKSXbHNkmInOg8aY2vr6+OHLkCHbt2oXz588jOTkZeXl5qFixIgIDAzFkyBA4OTlpI1YiIioBdcmpqaxGAnAXSSIyD1rZP10sFmPkyJEYOXKkNi5HRERaoDyh0MHBAeXKlTNQNCXHjW2IyBxoVEZCRETGy1TX2M7HZJuIzIFGI9uxsbGIiopCfHw8MjIyIBKJ4OzsDC8vL/j7+6NJkybaipOIiErIVHePzMdkm4jMQamS7Zs3b+Lzzz/HjRs3ALyZ8V6QSCQCADRu3BjffvstvLy8NAyTiIhKiiPbRESGV+Jk+8qVKwgJCcGrV69QtmxZtG3bFvXr14erqysEQUBaWhpu3LiBM2fO4N9//8XgwYOxceNGNGjQQBfxExFRIUx52T9A9cvBixcvIJVKYWNjY6CIiIhKrkTJdl5eHj799FO8evUKAwYMwKxZsyAWi9WeK5FIsHDhQuzevRvTp0/HwYMHYWXFEnEiIn0x9WRbXdlLSkoKKlasaIBoiIhKp0TZ799//43ExET06NED8+fPLzTRBt6sUPLtt9+iW7duuHfvHk6cOKFxsEREVHzKybaplZG4ubnJyxLzcfk/IjI1JUq2T548CSsrK0ydOrXYz5k+fTqAN4k6ERHpj3KybWoTJMuUKQM3NzeFNuVJn0RExq5Eyfa1a9dQs2ZNVK1atdjPqVq1KmrVqoX//vuvxMEREVHpKY8Cm1oZCaA6Gs+RbSIyNSWq2X727BlatGhR4pvUqFEDMTExJX4eERGVjiAIOisjSUlJwdatW/H06VN5m729PbKzs1GmTBl06dIFAQEBKiUgpVG+fHnExcXJH3NFEiIyNSVKtiUSCRwdHUt8E7FYDIlEUuLnERFR6WRkZEAqlSq0aVpGcvPmTfz000/YsWMHACj8ltPKygoymQwSiQTff/89mjRpgo8//hh9+vSBra1tqe/J5f+IyNSVqIwkNze3VCMVVlZWyMvLK/HziIiodNQlpaUZ2RYEAcePH0f//v3RqlUrhIeHY/r06bh69SqioqLkf27evImoqChcvXoVO3bsgLu7O8aNG4emTZti2bJlpS7/YLJNRKaOa/EREZkh5YmE5cqVg4ODQ4mv0aNHDwwaNAipqan46aefcPnyZUyfPr3QxN3KygqdOnXCrl27cO7cOXTt2hXLli1D48aNsWvXrhK/DibbRGTqSrypzb59+7Bv3z4dhKLqypUrCAsLw+XLlyGVSuHl5YWQkBD06tWrWM//4IMPEB0dXeQ53333Hfr06SN/3LFjRyQnJ6s9d/DgwZg3b16x4yciMhRNd4+8efMmhgwZgtevX2Pv3r1o27ZtiX+zWa9ePaxYsQJffPEFvvjiC3z00UdISEjAjBkzin0tJttEZOpKnGwrb81eXCX9kI6KisKYMWNgY2ODnj17wtHREeHh4ZgxYwaSk5Mxbty4t16jb9++8PPzU2nPzc3F2rVrYWVlhcDAQJXjjo6OCAkJUWn38fEp0WsgIjIUTTa0OXXqFEJCQuDp6YkDBw6gSpUqGsXi7u6ONWvWoHbt2liwYAESEhKwYsUK2NnZFeu5BXE1EiIyNSVKtm/evKmrOBTk5ubiiy++gEgkwubNm+VbvU+YMAFDhgxBWFgYunXrhho1ahR5nX79+qltP3r0KARBQNu2bfHOO++oHHdycsKkSZM0fh1ERIZS2mR748aNmD59Otq2bYvffvsNTk5OWolHJBJhxowZqFmzJiZMmID79+9j48aNcHV1LfJ5ypM6uc42EZkao6zZjoyMxP379xEcHCxPtIE3q5qMHz8eubm52LNnT6mvv3PnTgDAgAEDNI6ViMgYlXTZP5lMhnnz5mHy5Mn44IMPsG3bNq0l2gX1798f+/btw82bN9G1a1fcvXu3yPOV405PT0dOTo7W4yIi0hWjTLbz66yDgoJUjrVu3VrhnJJ6/PgxIiIi4OHhgfbt26s9JycnB3v37sVPP/2ELVu26G1En4hIW0qye6QgCJg+fTq+//57fPPNN1i6dCmsrUtcZVhsAQEBCA8PBwB07doVCQkJhZ6rLm6WkhCRKdHdp6kG7t27BwCoXr26yjFnZ2e4uroiMTGxVNfevXs3ZDIZ+vbtW+gPk2fPnmHWrFkKbW3atMHixYtVtg4mIjJGJRnZ/uWXX/DHH38gLCwMw4YN03VoAIBatWrhyJEj6Nq1K4YNG4ajR4+q3cfB1dUVIpFIYb5QSkoKKlWqpJc4iYg0ZZTJdv4GOIVtoCMWi/H48eMSX1cQBHn5SWElJP369YOfnx+8vLxga2uL+Ph4rF69GqdPn8b48eOxdevWIid7Ojs7w8rKKH9hUCxvq58k7WFf64el9nNaWprC4xo1aqjti5MnT+Lzzz/H1KlTMXHiRI3uWdK+dnV1xV9//QV/f39MnjwZu3fvVvv5Wb58eYVa7ezsbIv9dwUs9z1tCOxr/THnvjbKZFtXIiMjkZSUBD8/P7Wj5gBUftg0adIEa9euxfDhwxETE4NTp04VWn4CvKknNFWurq4qP6BJN9jX+mHJ/VxwK3XgzXbqyn2RmJiIAQMGICgoCLNnz9aor0rb1xUrVsTatWsxbNgwzJ49W+W3igDg5uamkGzfu3fPYv9dLfk9rW/sa/0x5b4uzpcEoxyCFYvFAIDMzEy1x0u7bXxpJ0ZaWVnJVzaJjY0t8X2JiPRJEIS3rkby8uVLDB8+HE5OTvjtt990WqP9Nt26dcPnn3+OxYsX46+//lI5zrW2iciUGWWynb+kn7q67PT0dKSlpRU6Ml2Y9PR0HDt2DE5OTujatWuJY8r/5vLq1asSP5eISJ/S09ORm5ur0FZwoqEgCJgwYQLu3buHTZs2GcWvb6dNm4b33nsP48ePx/Xr1xWOMdkmIlNmlMl2y5YtAQBnz55VORYREQEAajerKcqff/6JnJwc9OrVC/b29iWO6cqVKwAAT0/PEj+XiEif1CWjBSdILl++HH/++Sd+/PFHheVVDUkkEmH16tWoUaMGhg0bhtTUVPkxJttEZMqMMtkODAxE1apVceDAAdy4cUPeLpFIsGbNGlhbW6Nv377y9tTUVMTHxyt8OCvbtWsXgKJLSO7cuYOMjAyV9osXL2L9+vWwtbVFly5dSvOSiIj0RjkZFYvFKFu2LADg2LFj+Pbbb/HZZ58hODjYEOEVqly5cti8eTMyMzMRGhoKmUwGgMk2EZk2o5wgaW1tjfnz5yM0NBRDhw5FcHAwxGIxwsPDkZSUhClTpqBmzZry8zdv3ozVq1dj4sSJand+vHbtGm7evImGDRsWOYpz+PBh/PLLLwgMDISnpydsbW0RFxeHiIgIWFlZYe7cuahcubJOXjMRkbYUtuxfRkYGpkyZgnfffReffvqpIUJ7q2rVquHnn39G//79sWHDBowcOZLJNhGZNKNMtoE3mx5s2bIFq1atwuHDhyGVSuHl5YXJkyejd+/eJbpWcUa1AcDf3x/x8fG4fv06oqOjkZOTA3d3d/To0QMjR45E48aNS/16iIj0pbBke+7cucjMzMSKFSuMeonSDh06YPjw4fjqq6/QpUsXlTXCuakNEZkSkVBwpwDSmKkuXQOY9tI7poZ9rR+W2s9Lly7FggUL5I+7du2KiRMnolevXli8eDFCQ0O1fk9t9/WLFy8QGBiI5s2b4+OPP1YYZHFycpJvfmZpLPU9bQjsa/0x5b422aX/iIio9JRHtl1cXDBlyhT4+/tj9OjRBoqqZFxcXLB48WIcPnwY//77r8KxjIwM5OTkGCgyIqKSYbJNRGRmlJPt+Ph4PHjwAN9//71Rl48o69WrF4KDg7Fy5UqVY6zbJiJTYTqfukREVCwFd1sEgJiYGMyYMQPe3t4Giqj0Fi9eDKlUCpFIpNCu/BqJiIwVk20iIjPz6NEjhceVK1fGJ598YqBoNFOxYkV88803UJ5epPwaiYiMFZNtIiIzIggCHj58qNA2Y8YM2NraGigizQ0fPhyOjo4KbcqvkYjIWDHZJiIyIxkZGcjKylJoa9eunYGi0Q6RSARfX1+FNo5sE5GpYLJNRGRG1I34VqxY0QCRaFedOnUUHnNkm4hMBZNtIiIzopyEuru7w97e3kDRaE+lSpUUHjPZJiJTwWSbiMiMKCehykmqqVJ+HXfv3jVQJEREJcNkm4jIjJw4cULhceXKlQ0UiXYpv47k5GTIZDIDRUNEVHxMtomIzER2djaOHz+u0GauI9symQybN282UDRERMXHZJuIyEysW7dOZSUSc022AeDbb7/Fq1evDBANEVHxMdkmIjIDKSkpWLZsGdzc3BTazaWMxMHBAS4uLgptz58/x9q1aw0TEBFRMTHZJiIyA4sXLwYAlZ0WzWVkG1B9LW3btsXy5cu5dTsRGTUm20REJu727dtYv349Jk+ejNTUVIVj5pxsN2/eHFZWVvIvGkRExojJNhGRiZs3bx4qVqyI4OBglWPmUkYCqCbb6enpmD59On7//XfExcUZKCoioqIx2SYiMmFRUVE4ePAg/ve//6mMajs4OMDZ2dlAkWmf8heHR48e4cMPP4Snpye++eYbA0VFRFQ0JttERCZs0aJFaNiwIfr164fk5GSFY5UqVYJIJDJQZNqnbhdJe3t7zJo1CwcPHsTly5cNExgRURGYbBMRmaiIiAicOnUKs2bNgpWVFR49eqRw3JzqtQH1I9sAMGDAAHh5eeG7774zRFhEREVisk1EZIIEQcDChQvRuHFj9OjRAwBUkm1zqtcGVF/P06dPIZVKYW1tjZkzZ+Lo0aOIiYkxUHREROox2SYiMkFnzpzBuXPnMHv2bHmpiLmPbCu/HkEQ8OTJEwBA37594e3tjUWLFhkiNCKiQjHZJiIyMfmj2s2bN0eXLl3k7Q8fPlQ4z9ySbTc3N9jZ2Sm05b/mMmXK4LPPPsPff/+NqKgoQ4RHRKQWk20iIhNz8uRJREVFYdasWQoTIM29jEQkEql8gSj4mt977z3Ur1+fo9tEZFSYbBMRmZD8Ue2WLVvi3XfflbfLZDI8fvxY4VxzG9kGVF9TwWTbysoKs2bNwqlTp3Du3Dl9h0ZEpBaTbSIiE3L8+HHExMQo1GoDwPPnzyGVShXOtYRkW7l0pmfPnmjUqBFHt4nIaDDZJiIyEfmj2oGBgWjXrp3CMeUSEisrK1SoUEGf4elFUSPbwP+Pbp89exZnzpzRZ2hERGox2SYiMhFHjhzB5cuXVUa1AdWk85133oG1tbU+w9OLwtbaLqhbt25o2rQpFi5cCEEQ9BUaEZFaTLaJiEyATCbDokWL0KZNGwQFBakcN/eVSPK9rYwEeDORcvbs2YiMjMTJkyf1FRoRkVpMtomITMCRI0dw9epVzJo1S+1xc1+JJJ+6MhJ1o9edOnVC8+bNsWTJEo5uE5FBMdkmIjJygiBg2bJlaN26NQIDA9WeozzCa67Jtqenp8Lj169fIy0tTeU8kUiETz/9FFFRUYiIiNBXeEREKphsExEZuRMnTuDSpUuYPn16oeeY++6R+d5555231qvn69KlC3x8fLBs2TJ9hEZEpBaTbSIiIyYIApYuXYoWLVqorEBSkKXUbNvY2MDDw0OhTV3dNvBmdHv69Ok4deoUoqOj9REeEZEKJttEREYsIiICUVFR+PTTT1VGdAuylJptQPW1FZZsA0CvXr3g7e2N5cuX6zosIiK1mGwTERmxZcuWoVGjRujcuXOh52RmZiIzM1OhzVxHtoG3r7VdkJWVFaZPn47w8HD8+++/ug6NiEiFUSfbV65cwYcffoiWLVuiadOmGDBgAP76669iPz8qKgp169Yt9M/ly5d1cl8iIm2Ijo7GqVOnMH369BKNagOWlWwXNbINAH379kXNmjVZu01EBmG0Ox5ERUVhzJgxsLGxQc+ePeHo6Ijw8HDMmDEDycnJGDduXLGv5efnBz8/P5X2ihUr6vS+RESaWLZsGerWrYvg4OAiz1NOtp2dneHg4KDL0AyqJCPbAGBtbY0pU6Zg8uTJuHHjBurXr6/L8IiIFBhlsp2bm4svvvgCIpEImzdvRoMGDQAAEyZMwJAhQxAWFoZu3bqhRo0axbqen58fJk2apPf7EhGV1r///otjx45h3bp1sLIq+peQllSvDRRvF0llgwcPxpIlS7BixQqsW7dOV6EREakwyjKSyMhI3L9/H8HBwfKEFwDEYjHGjx+P3Nxc7Nmzx2zuS0SkbNmyZahVqxb69Onz1nMtZSWSfCUtIwEAW1tbTJ48GXv27EF8fLyuQiMiUmGUyXb+Ek3qtiRu3bq1wjnFce/ePWzYsAHr1q3DgQMHkJqaqpf7EhGVxvXr13HgwAFMmTIF1tZv/wWkpayxnU/59b148QKvXr166/OGDRuGChUqYMWKFboKjYhIhVGWkdy7dw8AUL16dZVjzs7OcHV1RWJiYrGvd+DAARw4cED+2N7eHpMmTUJoaKhO70tEVBorVqxAlSpVMHjw4GKdb+llJMCbPqhVq1aRz7O3t8fEiRPx9ddfY+bMmahWrZquQiQikjPKZFsikQAAHB0d1R4Xi8V4/PjxW6/j5uaGmTNnon379qhcuTIyMjIQFRWFpUuXYsmSJRCLxRgyZIhW7+vs7PzW+kpj5urqaugQLAb7Wj9MrZ9v376NvXv3IiwsDBUqVCjWc548eaLw2MvLyyCvW1/3dHV1haOjo8Jyh5mZmcW6/5QpU7By5UqsXbsWa9as0WWYOmNq72lTxr7WH3Pua6NMtrWlTp06qFOnjvxx2bJl0bt3b9SrVw/9+vVDWFgYBg0apNXkOD09XWvX0jdXV1ekpaUZOgyLwL7WD1Ps57lz58LDwwN9+/YtduxJSUkKj52dnfX+uvXd1xUrVlRItuPi4tCkSZNiPXfcuHFYsmQJJk2apHZVKmNmiu9pU8W+1h9T7uvifEkwyiFYsVgMACqbNOSTSCSFjj4Xh7e3N5o0aYLnz58rlIXo+r5EREVJSkrC9u3bMWHCBNjb2xfrOVKpFE+fPlVoM/cyEqB0K5LkCw0NhZ2dHX744Qdth0VEpMIok+38pfXU1Uenp6cjLS1NbV11SeR/E8nOztbrfYmIChMWFgZHR0eMHDmy2M958uQJBEFQaDP3CZJAybZsV+bk5IQPP/wQv//+O1JSUrQdGhGRAqNMtlu2bAkAOHv2rMqxiIgIAFC7SU1x5ebm4vr16xCJRAo/lHR9XyKiwjx58gQbNmzAuHHj5L9lKw7lJNPOzg5ubm7aDs/olGb5v4LGjRsHQRDw008/aTMsIiIVRplsBwYGomrVqjhw4ABu3Lghb5dIJFizZg2sra3Rt29feXtqairi4+NVlvS7dOmSyohPbm4uFi9ejOTkZAQFBcHFxaXU9yUi0pY1a9bAxsYGH374YYmep1w+UbFixSK3djcXJd1FUpm7uztGjhyJn3/+GRkZGdoMjYhIgVFOkLS2tsb8+fMRGhqKoUOHIjg4GGKxGOHh4UhKSsKUKVNQs2ZN+fmbN2/G6tWrMXHiRIWdIqdPnw4AaNasGd555x1kZmbiwoULSEhIQOXKlTF37lyN7ktEpA1paWlYv349QkNDFQYAisPSlv3Lp0nNdr4JEybgl19+wa+//oqpU6dqKzQiIgVGmWwDQEBAALZs2YJVq1bh8OHDkEql8PLywuTJk9G7d+9iXWPIkCE4c+YMoqOjkZaWBmtra1SrVg3jxo3D6NGj4ezsrJP7EhGVxNq1a5GXl4ePP/64xM+1tN0j8ym/zidPniAvLw9lypQp0TWGDRuGH3/8EWPHjoWDg4O2wyQigkhQrrMgjZjq0jWAaS+9Y2rY1/phCv2ckZGBpk2bYvDgwVi4cGGJn//hhx9i9+7d8scTJkzAN998o80Qi0Xfff3kyRPUr19foe2///4r8ZeNxMRE+Pr64ptvvsG4ceO0GaJOmMJ72lywr/XHlPvaZJf+IyKyFOvXr8fLly8xceLEUj3fUstIPDw8VLayL00pSfXq1TFo0CCEhYXh9evX2gqPiEiOyTYRkYG8evUKa9aswfvvvw9PT89SXcNSy0isrKxUNqQp6Yok+aZMmYLHjx9j69at2giNiEgBk20iIgPZuHEjUlJSMHny5FI9XxAEldFcS0m2Ac1XJMlXp04d9O7dG6tWrUJubq42QiMikmOyTURkAK9fv8aqVavQv3//Uq9ylJaWplL6UNoRclOkjRVJ8k2fPh337t3Drl27NA2LiEgBk20iIgPYsmULHj16hGnTppX6GsrJpUgkwjvvvKNpaCZDWyPbAODj44Pu3btj+fLlyMvL0zQ0IiI5JttERHqWk5ODFStWoG/fvqhbt26pr6Nco+zh4QEbGxtNwzMZmu4iqezTTz/FnTt3sHfvXo2uQ0RUEJNtIiI927ZtG5KSkuQbb5WWpU6OzKfNMhIAaNq0Kbp06YKlS5dydJuItIbJNhGRHkmlUqxYsQK9e/dWWSe6pCx12b986pJtTbeOmDFjBuLi4vDnn39qdB0ionxMtomI9Gjnzp1ITEzEp59+qvG17t+/r/DY0pPtly9f4vnz5xpd09fXFx07dsTSpUshk8k0uhYREWDE27UTEZmb3NxcLFu2DMHBwWjYsKHG14uLi1N4XLt2bY2vqUwQBIhEIgBAeno6YmNjkZmZiczMTIVl8sqVK4c6deqgSZMmAICsrCxcvnwZTk5OcHR0hJOTE1xcXOTX0oYqVarAzs5OYUWW27dvw8PDQ6Prfvrpp+jevTsOHDiA3r17axomEVk4JttERHqye/duJCQkYP369RpfSxAElWTb29u7xNd5/fo17ty5g5s3byIxMREPHz5EcnIyHj58iIcPH2LOnDkYOXIkAODGjRvo379/odeaPXu2PNm+d+8egoODFY7b2dmhcuXKqFy5MgYPHozhw4cDAPLy8pCSkgIPD48SJeNlypRB7dq1cf36dXlbXFwcWrVqVexrqOPv74927dphyZIlCA4OhpUVfwlMRKXHZJuISA/y8vKwbNkydOvWDY0bN9b4eo8fP4ZEIlFoKyrZFgQBDx48wMWLF1GnTh00atQIAHDu3LkiE+iCkzDd3NzQsGFDODo6QiwWK6x8YmNjozCyLpPJ4OXlJR8Fz8rKwuvXr5GQkICEhAS0a9dOfm5CQgL8/Pzg4uKCunXrom7duqhfvz6aNWuGxo0bw97evtD46tSpo5Bs3759u9BzS2LmzJno2bMnjhw5gh49emjlmkRkmZhsExHpwb59+3Dnzh2sXbtWK9dTTiodHBwUapglEgkuXryImJgY+Z9nz54BeLOBS36yXbduXXmSW6tWLXh6eqJy5cry/1atWlV+TW9vb5w5c0ZtPK6urkhLS5M/9vHxQXR0tPzx69ev8fjxY/mIecHJoYmJiRCJRHjx4gWioqIQFRUlP2ZjY4O5c+di3LhxACCfAJk/Al6nTp0i+6W0AgMDERQUhCVLlqB79+5aLX8hIsvCZJuISMfy8vKwZMkSdO7cGc2aNdPKNZWTSi8vL3m5Q3x8PFq1agWpVKpwjrW1NRo1aqSwy2SlSpUQHx+v82TSzs4O1atXR/Xq1VWOvfvuu0hKSkJ8fDxu3bqFW7du4erVq4iJicHz588V4o2IiMD48ePRrl07tGvXTmUTH20l28Cb2u333nsP4eHh6Nq1q9auS0SWhck2EZGO/fnnn4iLi0NYWJjWrqlcr12wpKRmzZpwcnJC2bJl4efnhxYtWqBFixZqSzKMZcS2bNmy8PHxgY+Pj7xNEATcv38f7u7u8rZTp04hKSkJmzdvxubNm1Wuc//+fbx69Qply5bVOKagoCAEBARgyZIl6NKli9H0FRGZFibbREQ6lJeXh++++w4dOnRAy5YtNbqWIAi4ceMGDh06hG3btikce/r0qXzlECsrK5w/fx7u7u4mnSCKRCKVkfApU6bA398fp0+fxunTp3HlyhWF44IgID4+XiFp1+T+M2fORL9+/Ti6TUSlxmSbiEiHdu7cibi4OKxZs0bja/Xt2xenT59We2zGjBkKj8uXL6/x/YxRuXLl0KlTJ3Tq1AkAkJKSAn9/f6SmpsrPiYuLg4+PD2bNmoWHDx+if//+6NKlS6lGu9u1a4fWrVtj/vz56Ny5M1cmIaIS46cGEZGO5OTk4LvvvkPPnj3RvHnzEj33/v37WLVqFXJycuRtLVq0gJ2dHTp27KhyfqdOnUx6FLu03N3d5csN5rt9+zZkMhn279+PAwcOYNSoUahbty7Gjx+Pv//+W2F98LcRiUSYM2cO/vvvP+zbt0/L0RORJWCyTUSkI5s3b8b9+/cxe/bsYp0vkUiwadMm9OjRA02bNsXXX3+Nf/75R3580qRJuH37Nr744guF54lEItSqVUuboZsUdSuSiEQi7Ny5E1OmTEGVKlUgkUiwbds2DBw4EA0bNsTSpUuLff2AgAB06tQJixYtKlGiTkQEMNkmItKJV69eYenSpejfvz8aNGhQ6HmCIODChQuYPHkyGjRogE8++QSRkZEQiURo27atQumDi4sLxGKxyuTI6tWrF7kWtblTXl88Li4OIpEIPj4++N///ofLly/j0KFDGDNmDNzd3fHs2TNkZmbKz5fJZAqP1ZkzZw7u3LmD7du36+Q1EJH5Ys02EZEO/Pbbb3j69ClmzZpV5Hk3btxQmHhXu3ZtDB8+HIMGDUKlSpXUPkd5ebvS7BxpTpRf/507dyCTyeT11VZWVggICEBAQAAWLFiAkydPol69evLz//nnH4wYMQJ9+vTB6NGj1Zb8NGnSBL1798bixYsxYMAA2NnZ6fZFEZHZ4Mg2EZGWZWZmYuXKlRg2bJhKecd///2HnTt3yh83aNAAfn5+GDJkCA4cOIDo6GhMnjy50EQbUF32T7mMwtIov/7s7GwkJSWpPdfGxgZdunRBtWrV5G3h4eHIysrCli1b5JMvt23bhuzsbIXnzp49G8nJydiwYYP2XwQRmS0m20REWvbTTz8hMzNTvkKIVCrFvn37EBwcjDZt2mDq1KlIT0+Xn3/o0CGsWbMGrVq1KtYkR+WRbUtPtitUqAAnJyeFNuUvJEVZuHAhDh06hMGDB8PW1haxsbEYP348GjVqhG+++UaedNetWxeDBg3C8uXLkZWVpdXXQETmi8k2EZEWpaWlYfXq1Rg1ahScnJywatUqNGvWDKNHj8a5c+dQpkwZdO7cWaFGuCTLyeXm5uLu3bsKbZZeRiISiTTatl0kEiEgIAA//vgjrl27hi+//BKenp5ISUnBwYMHFUpGZs6ciZSUFPzyyy9ai5+IzBtrtomItCgsLAx5eXlo2rQpfHx85Ds7enh4ICQkBCNHjkTlypVLff3ExESF5QABjmwDb75wxMTEyB+XZGS7oPLly2Pq1KmYNGkSjhw5Amtra/lvG7KysvD555+jXbt2+P777zFy5EiVEXUiImVMtomItOT+/ftYt24dxo4di6CgIGRnZ6NevXoYP348BgwYoJUVQ5RHbN3c3BS2M7dUmoxsq2NtbY3g4GCFtu3bt+PIkSMA3oyGf/jhh/j999+1sjU8EZkvJttERBoQBAFnz57FihUrcOfOHVhbW2PSpElwcXHByZMn0aBBA61uNqM8YmvpJST5lPtB02RbnW7duiEpKQm//fYb0tPTcezYMTRp0gQTJkyQlw0RESljzTYRUSnIZDIcOnQIXbp0wXvvvYd//vkHSUlJCA0NhYuLCwCgYcOGWt/VkZMj1VPuh2fPniEtLU2r96hUqRK+/PJLXL16Ff/73/9gZWWF58+fY+7cuWjSpInClvFERPk4sk1EVAJ5eXnYt28fli1bhps3bwIA7Ozs4OHhAQD49NNPdXp/Jtvq1ahRA9bW1go7PN6+fRt+fn5av5dYLMaUKVNgY2OD//3vf6hWrRrq168PNzc3+TmZmZlwdHTU+r2JyPRwZJuIqAS2b9+ODz/8EDdv3oRYLMbkyZOxevVqJCUlYf78+Trd7EQQBJaRFMLGxgY1a9ZUaCvtJMni+vDDD1GrVi3UqFEDq1evlrcnJSWhYcOGmDFjRqHrfROR5WCyTURUBKlUioSEBPnjfv36oUGDBpg9ezauXr2KOXPmYNmyZQgICECvXr10Gsvz58/x4sULhTaObP8/fdRtF2Rra4u5c+fi1KlTCiuh7N+/HxKJBL/99htatGiBKVOmIDExUaexEJHxYrJNRKSGVCrFpk2b4Ofnh8GDByMvLw8AYG9vjzNnzuDTTz+Fs7MzNm3ahJs3b2L+/Plar89Wppw82tnZKeyEaOn0nWwDQPfu3REUFIQvvvgCUqkUADBhwgT89ddfaNu2LaRSKTZs2ABfX19MmjSJSTeRBWKyTURUQG5uLrZu3YqAgAB88sknSExMRHp6usJGMvlJdUZGBhYuXIhBgwahefPmOo9NuSyidu3aKFOmjM7vayq0vfxfcYhEIsyfPx937txR2Ma9devW2LdvHw4dOoQOHTogLy8PmzdvRvv27bn7JJGFYbJNRIQ3Ex937tyJwMBATJgwAQkJCShfvjzmzZuHS5cuqS3XWLlyJSQSCb744gu9xMjJkUVT7o979+7h9evXOr9v48aN8f7772PhwoVIT09XOBYQEIDdu3fjyJEj6NChA0aOHAkHBwf58WfPnuk8PiIyLKNejeTKlSsICwvD5cuXIZVK4eXlhZCQkGLXRV68eBHHjx9HdHQ0kpOTkZWVBU9PT7z77rsYO3as2jVRO3bsiOTkZLXXGzx4MObNm6fRayIi43T+/HmMHTsWwJuNYj755BOMGTMG5cqVU3v+/fv38eOPP2LSpEmoUqWKXmJksl005f7Iy8tDQkIC6tWrp/N7z5kzR75KjbqfE35+fti9e7e8HAkAIiIi0L9/f4wcORJTpkxBxYoVdR4nEemf0SbbUVFRGDNmDGxsbNCzZ084OjoiPDwcM2bMQHJyMsaNG/fWa0yePBlpaWlo0aIF3nvvPYhEIkRHR+OXX35BeHg4tm3bpnbnNUdHR4SEhKi0+/j4aOW1EZHhCYKAu3fvonbt2gDe/Nq/R48eaNGiBUJDQ9+6bNu8efPg4uKCTz75RB/hAuCGNm/j5OSEihUr4vHjx/K2uLg4vSTblSpVwieffILly5dj1KhRKiuj5CtY9nP48GHk5ORg3bp12LhxI0JDQ/HJJ59wR1AiMyMSBEEwdBDKcnNz0b17dzx+/Bjbt29HgwYNAAASiQRDhgxBQkICDh48iBo1ahR5nXXr1qFPnz6oUKGCvE0QBMydOxdbt27F0KFD8dVXXyk8p2PHjgCAEydOlCp2bW+ioE+urq4mHb8pYV/rh7p+FgQBJ06cwIIFC3Dnzh1cvnwZrq6uJbpuVFQUunfvjlWrVmH48OHaDLlQWVlZqFq1Kgp+ZP/zzz9o3LixXu7/Nsbynu7Tpw9Onz4tfzxnzhxMnz5dL/d++fIl/Pz80KJFC4X67aKcPn0a3377LS5cuADgzRre48aNw4QJE+Ds7KxyvrH0syVgX+uPKfd1cX5+GGXNdmRkJO7fv4/g4GB5og28+RAaP348cnNzsWfPnrde56OPPlJItIE3k1nGjx8PAPIPNyKyDOfOnUPPnj0xcOBAXLp0CTKZDLGxsSW6hlQqxbRp09CsWTO8//77OopU1d27d6E8NpI/Kk//zxCTJPOVK1cO8+bNw4EDB3D06NFiPadt27Y4cuQItm/fjiZNmkAikWDp0qXo06ePyr83EZkmoywjiY6OBgAEBQWpHGvdurXCOaVhbf3mZRc2iz8nJwd79+7FkydP4OTkhObNm+vl15BEpBuxsbH49ttvcfLkSQBvlswbPXo0pkyZIt/5sbjWrFmDW7du4cSJE3pdCUS5hKRKlSqF1pNbMkMm28Cbddi3bNmCTz/9FEFBQcX6NxKJROjcuTM6deqEAwcOYOHChfjoo4/kq97k5uYiNzcX9vb2ug6fiHTAKJPte/fuAQCqV6+ucszZ2Rmurq4arVW6e/duAP+fuCt79uwZZs2apdDWpk0bLF68WGE7XiIyfk+ePEG3bt2Qm5sLa2trfPDBB5g+fToqV65c4mvdu3cPixcvxrhx4/RevsHJkcWjLtkWBEHna6DnE4lEWLp0KVq3bo3vvvuuRJPqRSIRevXqhR49eii0b968GUuWLMGMGTMwceJEbYdMRDpmlMm2RCIBgEInKInFYoUJMCVx48YN/PDDD3B3d0doaKjK8X79+sHPzw9eXl6wtbVFfHw8Vq9ejdOnT2P8+PHYunVrkR/azs7OsLIyyuqcYilp7SqVHvtad549eyYfsa5Xrx7GjBmDV69e4auvvkKtWrVKdU1BEDB79myUL18e3333HcRisTZDfqv8QYh8jRo1Mrr3kDHE07JlS4XHEokEWVlZelsxBnjTD//73//w5ZdfYsyYMWjatKlG19u5cycePnyIadOm4YcffsDXX3+N999/n2us64ExvKcthTn3tVFOkBw9ejQiIiIQHh6udnS7U6dOePz4Ma5du1ai6z548ADDhg1DWloafv75ZwQEBBTreTKZDMOHD0dMTAzWrl2L9u3bF3quqRb4A6Y9QcHUsK91IykpCUuXLsW2bdtw4sQJtG7dGmlpaVoZ2dyzZw9CQ0OxZcsWdOvWTUsRF1/btm0VPvOWLVuGUaNG6T2OwhjLe1oQBFSrVg0vX76Ut+3Zs6fIz21dyMnJQfv27eHg4ICjR49qlBhnZ2fj999/x4oVK+TrcterVw+zZ89GcHCw3kbtLY2xvKctgSn3tclOkMwfMcrMzFR7XCKRvHVZLmXJyckICQlBamoqVq1aVexEGwCsrKzQr18/ACjxZCoi0r2nT59i9uzZ8PX1xYYNG5CTk4PDhw/Lj2uajKSnp+Pzzz9Hr169DJJoy2Qy3LlzR6GNZSTqiUQig9dtA4CtrS1WrFiB2NhYrF+/XqNr2dvbY9y4cYiJicGCBQvg7OyMmzdvIiQkBJ999pmWIiYiXTHKZDt/ST91ddnp6elIS0tTO+JdmKSkJHzwwQd4+vQpVq5ciQ4dOpQ4pvxvLq9evSrxc4lIN9LS0vDNN9+gefPmWLt2LXJyctC6dWscOnRIq8u9zZs3D1lZWVi4cKHWrlkSSUlJyM7OVmhjsl04Y0i2AcDf3x8jR47EvHnz8OjRI42vJxaLMXv2bFy+fBnTp0+HWCzGwIED5cdzc3M1vgcRaZ9RJtv5NXdnz55VORYREQHgzW5cxZGUlIQRI0bg6dOnWLFiBTp16lSqmK5cuQIA8PT0LNXziUi7ZDIZOnfujBUrViArKwvNmzfH7t278eeff5boN1dvEx0djfXr1+PLL78s1aRKbVBeicTJyUllWVP6f8aSbAPA//73Pzg4OGD27Nlau6azszPmzJmDa9euKdSof/311+jfvz9iYmK0di8i0pxRJtuBgYGoWrUqDhw4gBs3bsjbJRIJ1qxZA2tra/Tt21fenpqaivj4eKSmpipcJz/RfvLkCZYvX47OnTsXed87d+4gIyNDpf3ixYtYv349bG1t0aVLFw1fHRGVVlZWlnztYSsrK4waNQoNGzbEpk2bcOzYMXTo0EGr9atSqRRTp05F8+bNMXr0aK1dt6Tyv+znq1OnDut0i6CcbF+5cgUymcwgsbi4uODbb7/Fn3/+Wey1t4vLyclJ/neJRIINGzbg5MmT6Ny5M4YNG1bieU1EpBtGuRqJtbU15s+fj9DQUAwdOhTBwcEQi8UIDw9HUlISpkyZorAV7ubNm7F69WpMnDgRkyZNkrePGDECycnJaNq0KW7duoVbt26p3Kvg+YcPH8Yvv/yCwMBAeHp6wtbWFnFxcYiIiICVlRXmzp1rsJEtIkuWnZ2NP/74AytWrMCSJUvQq1cvAG82rvr44491tgLQokWLcPv2bfz9998GXfmh4I6IANC8eXMDRWIaWrRoofA4LS0N165dM9hum/369cP27dsxdepUnDlzRifbsYvFYpw5cwaLFy/G9u3bcfjwYRw+fBjvvfcePvvsM+4VQWRARplsA0BAQAC2bNmCVatW4fDhw5BKpfDy8sLkyZPRu3fvYl0jOTkZAHD58mVcvnxZ7TkFk21/f3/Ex8fj+vXriI6ORk5ODtzd3dGjRw+MHDnSaLZFJrIUr1+/xqZNm7B8+XJ5zeuWLVvkybaNjY3O7h0REYGVK1fiiy++QKNGjXR2n7d59eoVoqKiFNratWtnoGhMQ5UqVVCrVi3cvXtX3mbIre1FIhG+//57tGnTBpMnT8bGjRt18puJ6tWr44cffsCUKVPw3XffYc+ePdi/fz/+/PNPrF69Wq87nhLR/zPKpf9MmakuXQOY9tI7poZ9XbScnBxs2bIFy5Ytk39p9vT0xPTp0zFs2LBiJ9ml7ecXL16gTZs2qFGjBvbt22fQUe1//vlHvhoS8KZ85u7duwolBMbA2N7T06dPV1gFpGPHjti1a5cBIwIOHTqE4cOHY/ny5Rg5cmSprlGSfr5+/ToWLVqEv//+GxcvXkSlSpUAvJnvYMr7QeiLsb2nzZkp97XJLv1HRJYtNDQU06ZNQ3JyMipVqoQlS5bg4sWLGDlypE5Hs4E36zRPmzYNEokEP/74o8E3Djl16pTC42bNmhldom2M2rZtq/D4/PnzeP36tYGieSP/t6Rz5sxRmfSqCw0aNMCGDRsQGxsrT7QBYPjw4ZgwYQISEhJ0HgMRMdkmIiOQk5OjsKzm8OHD8c4772DhwoWIiYnBmDFjYGdnp5dYtm3bhn379mH58uV63XWwMMr12vrenMVUtWnTRqFU49WrV7h48aIBI3rjm2++QZUqVTB27Fjk5OTo5Z7vvPOO/O+3bt3CkSNHsHXrVvj5+WHChAmIj4/XSxxElorJNhEZzOvXr/Hbb7+hRYsWWLNmjby9c+fOiI2NxdixY2Fvb6+3eBISEvDZZ5/h/fffV1jxyFDS0tJU5psoj9iSem5ubio12v/8849hgimgXLlyWLduHa5fv44FCxbo/f5169ZFeHg4OnXqhLy8PGzduhX+/v4YN26cQZdIJDJnTLaJSO9evXqFdevWoUWLFpgxYwaSk5OxZ88e+bJ+IpEIZcuW1WtMUqkUH330EcqXL49Fixbp9d6FOXv2LApOqylbtqzCuspUNOWJpMq/JTCUJk2aYM6cOQgLCzNITL6+vtixYweOHTuGLl26QCaTYceOHQgMDFQpWyIizTHZJiK9yczMxKpVq9CsWTPMmjULDx8+RKVKleSTuAy5dvSSJUtw+fJlrF27Fo6OjgaLoyDlxCcgIECvI/2mTjnZjo2NVbuXgiFMnDgRbdq0wccff6yyR4S+tGjRAtu2bcPff/+Nbt26oWLFigobQhlLXxGZOibbRKQ3//vf//D111/j6dOnqFq1KpYuXYqYmBh89NFHBk0ijx07huXLl2PmzJlGNXKsnGxzyb+S8ff3h62trfxxXl6efBdiQ7OyssIPP/yAV69eYezYscjLyzNYLM2aNcOWLVsQEREhnxuRm5uLjh07YsCAATh//rzBYiMyB0y2iUhnkpKSkJSUJH88btw4eHt744cffsDFixcxevRog4/U3rp1C6GhoejcuTOmTZtm0FgKSkpKUpm4xmS7ZBwcHODv76/QZkxlEp6envj111/xzz//4KuvvjJ0OHB2dpb/PSYmBomJiThx4gR69uyJHj164OjRowbbiZPIlDHZJiKtu3nzJiZMmIDmzZtj/vz58va6devi/PnzeP/993W+hF9xpKWlYejQofD09MTatWsNvsxfQcpJoaurq0E31zFVyhNKjSnZBoAOHTrg22+/xZo1a7Bp0yZDhyPn7++PCxcuICQkBLa2toiMjMT777+PoKAgbN26VW8rqRCZAybbRKQVgiAgMjISQ4cORatWrbB161bk5ubiyZMnCr8iN2RddkFSqRSjR49Geno6tmzZYnRrVytPnGvTpg03IikF5d8G3Lp1C48fPzZQNOp9+OGHCAkJwfTp0xEZGWnocORq1KiBFStW4NKlS5g0aRLEYrH8i/SlS5cMHR6RyeAnNxFpLDw8HF26dEGPHj1w5MgRiEQi9OrVC+Hh4di7d69RjRjnmzNnDiIiIvDHH3+gRo0ahg5HgSAIKsk2S0hKp2nTpioTXo1lVZJ8IpEI3333Hfz8/DBixAg8ePDA0CEpqFSpEubOnYtr167hq6++QnBwsEJ5zsGDBxXKxYhIEZNtItLY9evXERMTAzs7O4wYMQJRUVH4448/4Ovra+jQ1Prtt9/wyy+/YMmSJWjdurWhw1Fx8+ZNPHnyRKGNyXbpWFtbo02bNgptxlZKAgC2trb4/fffUa5cOQwdOhQSicTQIalwcnLC5MmTsWHDBnnbixcvMHbsWDRr1gyhoaGIiYkxYIRExonJNhGVyJ07dzBr1iwcOnRI3hYSEoJZs2bhypUrWLlyJby8vAwYYdHOnDmDzz77DB999BFCQkIMHY5ayslglSpVULNmTQNFY/rU1W0XXL/cWLi7u2PLli1ITEzExx9/bBKTEVNSUuDr64u8vDzs2bMHnTt3Rvfu3bFv3z5IpVJDh0dkFJhsE9Fb5eXl4ejRoxgwYAD8/Pywbt06rFy5Un7c1dUVM2fOhIeHh+GCLIZ///0XI0aMQFBQkMLETWOjroTEWGrdTZHybwUePnxotFuU169fH+vWrcOhQ4cwa9Yso/xSUFDt2rWxb98+nDp1Sj7xOSoqCqNHj0bTpk1x9uxZQ4dIZHBMtomoUGlpaQgLC4Ovry/ef/99nDhxAiKRCF27djWJRKCgq1evom/fvvDy8sIff/wBa2trQ4ekVm5urkqCwhISzXh7e6NixYoKbcZYSpKvW7duWLFiBX755Rd8/vnnJvH/WaNGjfDDDz/g33//xYwZM1ChQgU8e/YMtWvXlp+TkZFhEq+FSNuM86cNERmF0NBQnDx5EgDg4uKC4cOHY/To0UY3ofBtrl+/jr59+6JmzZrYtWuX0a08UlBsbKxKva5yGQSVjEgkQrt27bB9+3Z526lTpzBmzBgDRlW0ESNGQCaTYdq0aShTpgy++eYbk/jtRsWKFfH5559j+vTpiI2NRaVKleTHRo0ahadPnyIkJASDBg0y6v8PibSJI9tEBODNKPZPP/2Ep0+fytuGDh0KHx8frFy5EteuXcO8efNMLtG+ceMG+vTpgypVqmD37t0KG3cYI+UR1/r166NChQoGisZ8KH9hOXPmjEF3bSyOkSNHYvHixVizZg3mzp1rUqPCdnZ2CAwMlD9+/vw5IiMj8d9//2HmzJmoX78+Jk6ciIsXL5rU6yIqDY5sE1mwvLw8nDlzBlu2bMFff/2F169f4/Xr15g8eTIAoG/fvujXr59JjKipk59oV6xYEXv27IGLi4uhQ3orLvmnG8rJdnp6Oq5cuYJmzZoZKKLiCQ0NRW5uLj7//HNYW1tj6dKlhg6pVMqXL49r165hx44d+P3333Hr1i1s2bIFW7ZsQcOGDTFz5kz06tXL0GES6QSTbSILFB8fj23btmHbtm1ITk6Wt/v4+KBq1aryx6a8icqdO3fQu3dvlC9fHnv37oWbm5uhQ3qrly9fIjo6WqGtffv2hgnGzHh6eqJOnTq4ffu2vO3UqVNGn2wDwLhx45Cbm4v//e9/EIvFmDJliqFDKhVXV1eMHTsWH330EaKiorBhwwbs27cP//33n0LpVFZWFqytrWFra2vAaIm0h8k2kYV5+fIl2rZti1evXgEAnJ2dMWDAALz//vto1qyZyY5iFxQZGYkRI0agQoUK2Lt3L9zd3Q0dUrHs2bNHYbk0a2trhV/Fk2batWunkGxv374dkyZNMspNl5RNnDgReXl5mDt3LpKTk7Fw4UKjneT7NiKRCAEBAQgICMCCBQuwZ88ehVHt9evXY+XKlejfvz8GDhyI5s2bm8XnElku0x22IqK3evnyJXbv3o1Zs2bJ28qVK4fevXujU6dO+PXXX3Hjxg0sWbLEbH6gbd++HX369EGdOnVw6tQpo1+OMJ9UKsWyZcsU2lq1aqWy+yGVXufOnRUe37p1C/v37zdQNCU3efJkrF27Fn/88QcGDhyI9PR0Q4ekMRcXF4wePRpisVjedvz4caSkpGDdunXo3LkzfH19sWDBAsTFxRkwUqLSEwmcmaBVaWlphg6h1FxdXU06flOiy75+9eoVTpw4gf379+PQoUPIysoCAERERKB+/foAAJlMZtIlIurIZDJ8++23WLFiBYYOHYrly5fjnXfeMZn39IYNG1TKA3bt2oWOHTsaJqASMoXPD5lMhnbt2uG///6Tt3l7eyMiIsIkRreBN/28f/9+jBw5Eh4eHti6dStq1apl6LC0Kjc3FydPnsTOnTsVPsMAwM/PD4cOHdLL55cpvKfNhSn3taur61vPMa+ftkQWLCYmBmPGjIG3tzc++OAD7Nq1C1lZWahZsyZmzJihMDnQ3BJtiUSCkJAQrFy5EvPmzUNYWJhJ1Xvm5ORg+fLlCm0tW7ZEhw4dDBSRebKyssLMmTMV2uLi4rBv3z7DBFRKbdu2xbFjxyAIAjp16oQzZ84YOiStsra2RufOnbFu3TrcvHkT69atQ5cuXWBtbY2KFSsqfH799ttvCqVBRMaII9taZqrfzADT/mZparTR18+fP4dMJpMvC3f06FG8//77AN5MBuvVqxf69+9vNuUhhUlKSsKwYcOQkJCAn3/+GV27dpUfM5X39B9//IGpU6cqtO3evdukkm1T6WuZTIb27dvj2rVr8rY6derg3LlzJjG6XbCfX7x4gVGjRiEiIgKLFy9GSEiIWf+/npKSgszMTPnyo7dv34a/vz8AoF69eggODka3bt3QtGlTrQwomMp72hyYcl9zZJvIjAiCgJs3b+L7779Ht27dULduXfz000/y4+3bt8fUqVNx/PhxXLlyBQsWLECLFi3M9oevIAjYtm0bgoKCkJaWhsOHDysk2qYiJydHpVbbz8+Pq5DoiLrR7du3b2Pv3r0Giqj0XFxcsGPHDoSEhGDatGkYOXIknj17ZuiwdMbd3V1hnf/s7Gy8++67sLa2xs2bN7F06VJ06tQJPj4+mDJlCq5evWq4YIkK4Mi2lpnqNzPAtL9Zmpri9nVeXh6OHj2KEydO4MSJE7h3757C8T59+uC3337TUZTG68mTJ5g2bRoOHz6MQYMGYdGiRWrX0DaF9/Tvv/+OadOmKbTt2bPH5JJtU+jrfIIgoH379grJmKmMbhfWz/v27cOMGTNgZWWFpUuXonfv3gaIzjBevHiBI0eO4MiRIzhx4oR8GcEtW7agW7duAN78BiwrKwt16tQp9gCEKb2nTZ0p93VxRraZbGuZqb5ZANN+s5uawvo6Ly8PDx8+lK91LZPJUL9+fflola2tLdq2bYtu3bqhS5cuqFKlil7jNjRBELBnzx7MnDkT1tbWWL58OXr27Fno+cb+ns7JyYGvry+SkpLkbQEBATh48KDJ/UbC2Pta2aFDhzB8+HCFtrVr12LgwIEGiqh4iurnp0+fYsaMGThw4AD69++P7777ziTWl9em169fIyIiAuHh4fjf//4HBwcHAMC8efOwcuVKVKlSBR07dkTHjh3Rpk2bIhMlU3tPmzJT7msm2wZgqm8WwLTf7KYmv69lMhmuX7+OM2fO4OzZszh37hzs7e1x/fp1ebI1b948ZGZm4t1330VQUJDCElmW5MmTJ/jss8/w559/4r333sPSpUvfun62sb+n169fj+nTpyu07d271yR3jTT2vlYmCAI6dOiAK1euyNu8vLxw7tw5o16/+m39LAgCdu3ahZkzZ8Le3h5Lly5Fjx49TO7Lm7bNnDkTGzZsQE5OjrxNJBLBx8cHQUFBmDNnjjwxz2dq72lTZsp9zWTbAEz1zQKY9pvd1Bw7dgwbNmzA+fPnkZqaqnDM0dERZ86cQbVq1QwUnXHJyMhAWFgYfvrpJ9jZ2WHx4sXo169fsZ5rzO/p169fw9fXV2EHz8DAQBw4cMAkEyNj7uvCHD58GMOGDVNo++mnnzBo0CADRfR2xe3nR48eYcqUKTh27BhatWqFr776Ci1bttRDhMYrKysLEREROHHiBE6ePClft7t8+fK4deuW/P+7LVu2oFy5cujatSvs7e0NGbLFMMXPj3zFSbaN9+s7kRl49OgRLl26hIsXL2Ly5MlwdnYGAMTGxuLgwYMA3mwyExAQgDZt2iAoKAiNGzc26pE1fcnOzsZvv/2G5cuXIysrC2PHjsXkyZPV1mabos2bNysk2gDw2WefmWSibaq6deuGJk2a4N9//5W3LVmyBP369TP5/wcrVaqEbdu24fjx45g3bx66du2Knj17Ys6cOahXr56hwzMIBwcHdO7cWb650ZMnT3D27FlkZmbK/78TBAHz58/H48ePAQDVq1eHv78//P390bx5czRo0AA2NjYGew1kmjiyrWWm+s0MMO1vlsYgIyMDly5dQmxsLC5duoSYmBg8evRIfnz79u3yD/k7d+7g0KFDCAgIQLNmzfjhXUBubi527NiBRYsW4dGjRxg+fDg+/fRTVK5cucTXMtb39LVr19C3b1+kpKTI21q1aoW//vrLZJNtY+3rtzly5AiGDh2q0DZ+/Hh88803RvlvUZp+lslk2L17NxYsWIAHDx5gyJAh+Oyzz+RzQ+j/ZWdn48svv0RUVBT+++8/KKdIrVu3xl9//SV/fO/ePVStWtXoJ9YaO1P9/AA4sk2kEzKZDA8ePMDVq1fRuHFjebnHX3/9hUmTJimca2VlhXr16qF58+YK24a3bNkSXl5eeo3b2D169AgbN27EH3/8gUePHuG9997DnDlzzK6fIiMjMWTIEGRkZCi0z5o1yyiTO3PXtWtXNG3aFJcvX5a3rVmzBhkZGVixYoVZJFFWVlYYOHAg3nvvPfzxxx9YunQptm3bhu7du2P06NFo166d2W10VVr29vZYsmQJgDf9duzYMURFReHixYu4dOkSGjVqJD83MzMTLVq0gIODAxo0aIBGjRrBx8cHjRo1Qv369VVqwMlycWRby0z1mxlg2t8sdSUjIwPR0dG4efOm/M+tW7fw8uVLAMDixYsRGhoKALh69SpGjBiBZs2aoXnz5mjRogUaNWqkdkIj+/oNQRBw9uxZ/Prrrzh06BDs7OwwcOBAjBkzBg0bNtT4+sbWz8ePH0dISAhevXql0N65c2ds377dQFFph7H1dUmcPn0affr0UWnv1asX1q1bBzs7O/0HVQht9LNEIsHOnTvx66+/4vr166hduzZGjRqFoUOHmk2ZljYo97UgCHj16pU8if7333/Ro0cPlf+fgTeTLydPnoz//e9/AN6sPHTr1i14eXmhbNmy+nkBJsSUPz84QdIATPXNApj2m720BEFAamoqEhIScPfuXdy+fRutWrWS79wXGRmJHj16qDzPxsYG9erVw0cffaQywao4LLGv8wmCgGvXruHAgQPYt28fbt++DW9vb4wZMwaDBw+Gk5OT1u5lTP28Z88ejBs3Drm5uQrtrVq1wpYtW7T6ug3BmPq6NLZu3YpPPvkEeXl5Cu3t27fHhg0bjGYVIG32syAIiIqKwq+//oo///wT1tbW6NGjB3r27Il3330Xjo6OWrmPqSpOX+fl5SE+Ph5Xr17FtWvXcPXqVVy9ehXPnj3DggULMG7cOADAlStX5GvnV6lSBbVr10adOnVQu3Zt1K5dG40bN5bvBmyJTPnzg8m2AZjqmwUw7Td7YQRBQEZGBvLy8uTrzSYlJeHzzz/HvXv3cO/ePfkGCPnGjRuHBQsWAADS09PRtWtX1KtXD/Xr10e9evVQr1491KpVS6M6a3Ps66Lk5uYiKioKBw8exKFDh3D//n04OTmhW7duGD58OFq3bq2TEgpj6efff/8d06dPV6n/7Nq1K3777TezGOkylr7WxKFDhzBmzBi8fv1aob1FixbYsWNHsX6o6pqu+vnp06fYvHkz9u3bh6tXr8LOzg7t2rVDjx490L17d4UyOEuhSV8/e/YMNjY28t8U/P333/jwww/x4sULted/8803mDBhAgAgPj4eq1atQo0aNVC1alVUqVIFVapUQcWKFU1+4m5hTPnzw+ST7StXriAsLAyXL1+GVCqFl5cXQkJC0KtXr2JfQyaTYcuWLdi+fTsSExPh4OAAf39/TJ06VWHbV23d11TfLIDpvdkFQZAnaBkZGdi9ezcePnyIx48f49GjR3j48CGSkpIgkUjw8ccf49tvvwXw5oeK8mz8SpUqoVatWvDy8sK7776L4OBgncZuan1dUq9fv8a///6LyMhIREVFITIyEmlpaahUqRJ69OiBHj16ICgoSOcTQw3Zz69fv8ahQ4ewadMmnDx5UuX4oEGDEBYWZjaTY83lPX3mzBkMGzZM5Ut4pUqVMHToUAwbNqzQnx36oI9+TkxMlH8xjoyMhCAIaNy4Mfz9/REQEICAgABUrFhRpzEYA233tSAISElJwZ07d3Dnzh3Ex8fL/z537lx06dIFALB//36MGjVK5flWVlaoVKkS5s6dK1/+9OHDhzh37hwqVqyIihUrokKFCnB0dDS5+R+m/Plh0sl2VFQUxowZAxsbG/Ts2ROOjo4IDw9HUlISpk6dKv/VzNt8+eWX2LFjB7y8vNCuXTukpKTIa0O3bdumMvlK0/ua6psFMOybXRAEvHz5EjKZTP7r9MzMTGzfvh0pKSlITU3F8+fP8ezZMzx9+hTPnj3DwIEDsWjRIgBvRhHq1q1b6PXff/99/PDDD/J7/frrr6hevTqqV6+OqlWr6n1k0ZQ/WJRlZ2fj9u3buHHjBq5fv46LFy8iNjYW2dnZKFeuHHx9feHv74/OnTujWbNmep2IZYh+/u+//7Bp0ybs3LlTZQ31fB999BEWLFhgVpPSzOk9fenSJQwcOLDQf7+2bdti2LBhCA4ONvvPjufPn+Po0aM4d+4cIiMjkZCQAACoWbMm/P395ZMB69evjwoVKphcklcUQ72nr1+/jr/++guJiYlISkpCUlISkpOTIZVKAQB//PGHfPDvzz//xMiRIxWeb2dnBw8PD1SoUAEzZ86UJ/FJSUk4ffo03N3d4ebmBjc3N7i4uMDZ2dngI+am/Plhssl2bm4uunfvjsePH2P79u1o0KABgDeTOoYMGYKEhAQcPHjwraMLkZGRCAkJga+vL9avXw9bW1sAwPnz5zFq1Cj4+vpi06ZNWr2vqb5ZgNK/2fPy8pCamoqsrCy8fPkSmZmZkEgkkEgkePnyJWrVqoWAgAAAQEpKCj799FNkZGQgIyMD6enpePHiBV68eAGpVIrRo0dj6dKl8nPr1KlT6H3fe+89rF+/HsCb32CMHDkS77zzjvwbfqVKlVC1alV4enoa3axwU/tgyc7ORlJSEu7fv4/79+8jKSkJcXFxuHnzJu7evQuZTAbgTS1is2bN5KNfPj4+Bh251UU/y2QyvHr1CikpKbh7967Cn9u3byM+Pr7I58+cOdMs19M2tff029y6dQv9+vVTWL5TmaOjIxo2bIhatWrJ/9SuXRtVqlSBWCzWyXvf0P38+PFj+W+roqOjcePGDWRnZwMA3Nzc5KV21atXR7Vq1VC1alVUq1YN7u7uJveeN3RfFySTyfD06VMkJSWhVq1a8rLIv//+G6tWrcKjR4/w+PFjld/I/P777+jduzcAYN++fRg9erTa6zs6OmLZsmUYMGAAgDcT/n/88Uc4OTnJ/4jFYjg6OqJcuXLw8fFBlSpVALyZ/JmVlYVy5cqV+j1vTH1dUiabbJ89exZjxoxBv379sHDhQoVjhw4dwtSpUzF27FhMmzatyOtMnz4dBw4cwKZNm1R2zgoNDcWZM2dw5MgR1KxZU2v31eeb5d9//8Vvv/2G+/fvQyaTQRAECIKg8PeyZcvKR16kUimePXsGmUym9k+FChXk9WXZ2dm4ceMGZDIZ8vLykJeXp/D3qlWryn8rkJWVhVOnThUaZ9WqVeHj4wPgzf+Uf//9d6HnVqpUCU2bNgXwZgT60qVLsLW1ha2tLWxsbGBnZwc7OzvY2trC3t7eZH8Fb2NjIx+l0DdBEJCbm6vwRyqVQiqVIicnB69fv1b5r3INq729PcqVKyf/8BWLxTpLLpRjL8m56vo5//8NdX/y3+P5/SKTySCVSuVfJPP/lOZj08nJCXPnzkVISEiJn2sKTPmHZWEePHiAjz/+GOfOnSvV821tbVGuXDn5H3t7e5QpUwZlypSBtbU1rK2tYWVlBSsrK4hEIrV/lNnY2KhMsn0bXSa5giAgKysLEokEmZmZ8sGWV69eKUw2LVOmDOzt7eWf37a2tgp/t7a2ho2NjcJ/Df2bH0N+TpdWXl6e/DM7JycHzs7O8l0wnz9/joSEBEilUrx+/RpSqVTh36hZs2by8qBHjx4pLIepzMfHR75O+7Nnz3Dx4kUAb95r+e/xMmXKwMrKCl5eXqhUqRKAN7+xjo+Plx/L/2NjYwNBEODu7i7PQ3JycpCSkgKRSCT/f6Tg/y/29vby15afx3Tq1AmjR4/W65KdJrvOdnR0NAAgKChI5Vjr1q0VzilKVFQUHBwc0Lx5c5VjQUFBOHPmDC5cuCBPtrV1X31ISUlB//79C/01Z2m8bUSuoNu3b+P27dvFOvfBgwd48OBBsc599OhRkSNJZByys7ORnZ2tsCkLqRcUFIRhw4ahV69eRvcbFipa1apVceDAAVy9elVeGlTYBDd1cnJykJOTY3ZfQkojLy9P/mWVjNOlS5eKfe61a9dw7do1lfaCgzn5Ll++XGTirk3Hjh1Ddna2yp4XhmaUyfa9e/cAvNkmVZmzszNcXV2RmJhY5DWysrLw7NkzeHt7q/2Gk18Kkn8vbd3X2dlZL9/GExIStJpoE5H2VK5cGSNHjsSoUaPMblOeohjDah260LZtW7Rt2xarVq3Cvn378Ouvv+L48eOGDouI1Lhx44bRfRYZZbKdX3NU2BqfYrEYjx8/LvIamZmZ8nMLu0bBe2nrvunp6UUe15aKFSuifv36uHHjhl7uR0SqxGIxateurVCz6+XlhWbNmsknHFnKqKY5lpGo07VrV3Tt2hXJycm4cuUK4uPjcffuXSQkJCA+Ph7JycmlKjMiIu3o2bOnXj+LTLaMhN6ubNmy8qWZnjx5orVrqtsJi7SPfa0fDg4Ohe7ull/HWrA+Nr+WtmDNobW1tULdbX59uqOjI1xcXExu0hdph6enJzw9PVXas7OzkZqaqlDjnz9hPCcnRz4nIH/+S/7cgPx5AwAU/q7M3t6enx16ws9p/dFGX5cpUwZt27aVz/syJkaZbOePOuePTiuTSCRv3dkq/7jyzNyC1yh4L23dV59cXFwwdOhQrV3PUkamjAH7Wj/Yz6Rv9vb2qFy5ss6uz/e0/rCv9cfc+9ooF3nNr6dWVx+dnp6OtLQ0tXXVBTk4OMDDwwNJSUkq2+8C/1+fXXAZP23cl4iIiIgon1Em2/nL9J09e1blWEREBADAz8/vrdfx8/NDVlYWYmNjVY7lX7vgkoDaui8REREREWCkyXZgYKB8yaWCEwAlEgnWrFkDa2tr9O3bV96empqK+Ph4ldU5Bg0aBABYuXIlcnJy5O3nz5/H2bNn0bJlS/myf6W5LxERERFRUYyyZtva2hrz589HaGgohg4diuDgYIjFYvm26VOmTFFIkjdv3ozVq1dj4sSJCmsrBgQEYODAgdi5cyf69u2rsF27WCzG119/rdF9iYiIiIiKYpTJNvAmUd6yZQtWrVqFw4cPQyqVwsvLC5MnT5ZvPVoc8+bNQ926dbF9+3Zs3LgRDg4O6NChA6ZOnao2cdbWfYmIiIiIjHK7dlNmyrNpzX02sDFhX+sH+1l/2Nf6wX7WH/a1/phyXxdnnW2jrNkmIiIiIjIHTLaJiIiIiHSEyTYRERERkY4w2SYiIiIi0hEm20REREREOsJkm4iIiIhIR5hsExERERHpCJNtIiIiIiIdYbJNRERERKQjTLaJiIiIiHSE27UTEREREekIR7aJiIiIiHSEyTYRERERkY4w2SYiIiIi0hEm20REREREOsJkm4iIiIhIR6wNHQAZp3Xr1iEyMhLx8fFIS0tD2bJl4enpiV69emHIkCEoW7asoUM0eVlZWTh27BhOnDiBmzdv4tGjR7C1tUW9evUwZMgQBAcHGzpEs3LhwgWcOHEC165dw/Xr1yGRSNC3b18sWrTI0KGZrCtXriAsLAyXL1+GVCqFl5cXQkJC0KtXL0OHZjb279+PmJgYXLt2DXFxcZBKpVi4cCH69etn6NDMypMnT3D48GGcPn0ad+/exfPnz+Hs7IzmzZsjNDQUTZo0MXSIZiMjIwOrVq3C1atXkZSUhPT0dLi6uqJmzZoYNmwYunTpApFIZOgwtYpL/5FaHTt2hKurK7y9veHu7o6srCxER0fj9u3bqFevHrZt28aEW0OnT5/Ghx9+CBcXFwQGBqJq1apISUnBsWPHkJGRgeHDh+PLL780dJhmY9asWdi7dy/Kli2LSpUq4e7du0y2NRAVFYUxY8bAxsYGPXv2hKOjI8LDw5GUlISpU6di3Lhxhg7RLHTs2BHJyclwdXWFg4MDkpOTmWzrwNKlS/Hzzz+jWrVqaNmyJdzd3ZGYmIjjx49DEAQsW7YMPXr0MHSYZiExMRF9+vRBkyZNUK1aNbi4uCAlJQUnT55ESkoKBg0ahG+++cbQYWqXQKRGdna22vZPP/1U8Pb2FjZt2qTniMzPjRs3hD///FPIyclRaH/27JnQoUMHwdvbW/j3338NFJ35uXLlihAXFyfk5uYKly5dEry9vYXPPvvM0GGZJKlUKnTq1Enw8fER/vvvP3l7Zmam0LNnT6FBgwZCQkKC4QI0IxEREUJSUpIgCIKwdu1awdvbW9i9e7eBozI/R48eFS5cuKDSfuHCBaFhw4aCn5+f8Pr1awNEZn5yc3MFqVSq0p6ZmSn06NFD8Pb2FuLi4gwQme6wZpvUsrOzU9vetWtXAG++mZJm6tWrh169esHGxkahvXz58hg8eDCAN6UPpB2NGjVCnTp1UKZMGUOHYvIiIyNx//59BAcHo0GDBvJ2sViM8ePHIzc3F3v27DFghOajVatW8PT0NHQYZq9Lly7w9fVVaff19YW/vz9evHiBW7duGSAy81OmTBlYW6tWMYvFYgQFBQEwvxyDyTaVyKlTpwAAderUMXAk5i3/g4iJIRmj6OhoAJD/YCyodevWCucQmbr8z2N1CSJpz+vXrxEZGQmRSAQvLy9Dh6NVfOdQkX7//XdkZmYiIyMDsbGxuHbtGoKCgtCnTx9Dh2a28vLysG/fPohEIrRq1crQ4RCpuHfvHgCgevXqKsecnZ3h6upqdiNTZJkePnyIc+fOwcPDA97e3oYOx6xkZGTgjz/+gEwmQ0pKCk6fPo1Hjx5h4sSJqFGjhqHD0yom21SkDRs2IDk5Wf64d+/e+Prrr1VKH0h7vv/+e8TFxaF///78cCejJJFIAACOjo5qj4vFYjx+/FifIRFpnVQqxcyZM5GTk4MZM2bwN41alpGRgdWrV8sf29jYYObMmRg9erQBo9INJttmLL/OrLg2bNgAf39/hbYTJ04AAJ49e4aoqCgsWbIEgwYNwq+//oqKFStqM1yTpY1+zrd9+3asXbsWDRo0wJw5c7QUofnQZl8TERVGJpPh888/x4ULFzBo0CD+NlcHqlSpglu3biEvLw+PHj3CoUOHsGLFCly6dAkrV640q7Id83klpCI4OBgvX74s9vnly5cv9JiHhweCg4NRrVo1DBw4EIsWLcLKlSu1EKXp01Y/7969G1999RW8vb3x22+/oVy5ctoK0Wxo8z1NpScWiwEAmZmZao9LJJJCR72JjJ0gCPjiiy/w559/onfv3pg7d66hQzJrZcqUQZUqVfDRRx/BysoKS5YswY4dOzB06FBDh6Y1TLbNmC7WaG7cuDGcnZ05+akAbfTzrl278OWXX8LLywt//PEHXF1dtRCZ+eG648Yhv54yMTERPj4+CsfS09ORlpaGZs2aGSAyIs3IZDLMmTMHe/bsQXBwMBYtWgQrK64loS9BQUFYsmQJoqOjzSrZ5juISuTly5fIzMxk7ZoW7dq1C1988QVq1aqFP/74A25uboYOiahILVu2BACcPXtW5VhERAQAwM/PT68xEWmqYKLdo0cPLF68mD/r9OzJkycAzG8lLibbpCI5ORlJSUkq7VKpFAsWLIBMJkPbtm0NEJn52blzp0Ki7e7ubuiQiN4qf8fTAwcO4MaNG/J2iUSCNWvWwNraGn379jVghEQlUzDR7tatG5YsWWJ2CZ+xuHHjhtoStBcvXmDFihUAYHY5BrdrJxXHjx/HpEmT4Ovri+rVq8PV1RXPnz/H+fPn8ejRI9SsWRObNm1iPayGzp8/j1GjRkEQBAwePBgeHh4q59SvXx+dOnUyQHTm5+LFi9i1axcAIDU1FadOnUK1atXQokULAECtWrXw0UcfGTJEkxIZGYnQ0FDY2NggODgYYrFYvl37lClT8PHHHxs6RLOwc+dOxMTEAADi4uLw33//oXnz5vJlFzt16sTPCC0ICwvD6tWr4eDggBEjRqidnNepUyfUr1/fANGZl2+//Ra7du2Cv78/KleujLJly+Lhw4f4559/kJWVha5du2LlypVmVb7Dmm1S0aBBA4wYMQIXLlzAsWPHkJmZCQcHB9SuXRvDhg3DsGHD4ODgYOgwTd6jR4+Q/113+/btas/p27cvf5Bqyf3797F3716Vtvv37wN4U/bAZLv4AgICsGXLFqxatQqHDx+GVCqFl5cXJk+ejN69exs6PLMRExOj8r6NjY1FbGwsAMDT05OfEVqQv8RtVlYWfvrpJ7XneHp6MtnWgq5du0IikeDy5cu4cOECsrOz4ezsjBYtWqBPnz7o2bMnRCKRocPUKo5sExERERHpiPmM0RMRERERGRkm20REREREOsJkm4iIiIhIR5hsExERERHpCJNtIiIiIiIdYbJNRERERKQjTLaJiIiIiHSEyTYRERERkY4w2SYiIiIi0hEm20RERG+xZ88e1K1bF2FhYYYOhYhMDJNtIiIqkQ8++AB169ZFUlKSoUMhIjJ6TLaJiIiIiHSEyTYRERERkY4w2SYii5KUlIS6devigw8+gEQiwfz589GuXTs0atQI3bt3x++//w6ZTKbyvBs3bmDx4sXo168fAgIC4OPjg3fffRdff/01njx58tb7LFq0CB07dkTDhg3x7bffAgAyMjKwceNGjBkzBh06dICPjw/8/f0xZswYREREqI2/YAnHoUOH0L9/fzRp0gRt2rTB4sWLkZOTAwC4f/8+pk2bhsDAQDRp0gQjRozAzZs3C+2XEydOYMyYMfD390ejRo3QtWtXrFy5Ei9fvlR5TdHR0QCAd999F3Xr1pX/KUgQBOzduxfDhg2Dr68vGjdujF69euHXX3+FVCpVuX/Hjh1Rt25dCIKAjRs3onfv3mjSpAnee++9QmMGgHHjxqFu3bo4ffq02uM5OTnw8/ND48aNIZFI5LEdOHAAU6dORdeuXdG0aVM0a9YMAwYMwObNm9X++xemqJKagu8BdYrT50Rk+qwNHQARkSHk5ORgxIgRePDgAQICAiCVSnH+/HksXLgQt27dwsKFCxXOX7duHY4ePYo6deqgefPmEIlEuHHjBrZu3Yrjx49j9+7deOedd1Tuk52djeHDh+Phw4do2bIlGjZsCGdnZwDA5cuXMX/+fFSqVAk1atRA06ZN8ejRI0RERCAiIgLz58/HgAED1Ma/YcMGbN68GY0aNUJQUBBiYmLw66+/IiUlBR9//DGGDBkCsVgMX19f3Lt3D1FRUQgJCcHBgwdRvnx5hWstWrQI69evh52dHRo3bgxXV1f8999/+PHHH3H69Gls2rQJDg4OcHBwQN++fXHmzBk8f/4cXbt2hYODg0psMpkMU6dOxZEjRyAWi9GoUSM4ODjgypUrWLx4MaKiovDTTz/Bykp1vOerr77Cnj170LJlS9SuXVttYl5Qr169cPLkSfz1119o27atyvHTp08jPT0d3bp1g1gsBvDm33769OlwdnZG7dq10aBBA6SlpeHy5cuYN28erl69ikWLFhV5X00Vt8+JyAwIREQW5MGDB4K3t7fg7e0t9OrVS0hJSZEfS0xMFIKCggRvb2/h+PHjCs87d+6c8OTJE4W2vLw8ISwsTPD29hZmzZpV6H0GDx4spKenq8Ry//59ISYmRqX9v//+E3x9fYXmzZsLEolE4djw4cMFb29voVmzZsKFCxfk7U+fPhVatWol1K1bV+jevbuwcOFCIS8vTxAEQZDJZMLMmTMFb29v4fvvv1e43sGDBwVvb2+hT58+woMHD+TtOTk5wpdffil4e3sLixYtUhtDwfMLWrduneDt7S2MGjVKoX9fvnwpjB07VvD29hY2bdqk8JwOHToI3t7egr+/vxAXF6f2uuq8evVKaNq0qdC0aVMhKytL5fgnn3wieHt7C8eOHZO3SaVS4ejRo8Lr168Vzk1JSRH69esneHt7C9HR0QrHdu/eLXh7ewurVq1SaC+qL/LfA8OHD1doL02fE5HpYhkJEVmszz77DG5ubvLH1apVw/jx4wEAW7ZsUTg3MDAQFSpUUGizsrLCxIkT8c477+DEiROF3mfOnDlwcnJSaa9atSqaN2+u0t6gQQMMHToUEokEUVFRaq85cuRI+Pr6yh97eHigV69eEAQBUqkUM2bMkI8ci0QijBo1CgBw4cIFheusXbsWALBs2TJUqVJF3m5jY4M5c+bAw8MDu3btKnZpRW5uLn799VeUK1cOS5cuVehfBwcHzJ8/H7a2tti2bZva53/44YeoU6dOse4FAPb29ujcuTOysrJU/g0kEgn++ecfODs7K4x6W1tbo0uXLrC1tVU4383NDdOnTwcA/P3338WOoaS03edEZNxYRkJEFsnFxQWtW7dWae/Vqxe+/vprxMbGQhAEiEQi+bG0tDScOHECt2/fRkZGhjwZys3NxYsXL/DixQu4uLgoXM/DwwONGjUqNI68vDycP38ely5dwrNnz+Q11/fu3QMAJCYmqn1eq1atVNqqVq0KAPDz84O1teLHe7Vq1QAAT58+lbelpKTg5s2bqF27NmrVqqVyPTs7O/j4+ODkyZO4d++e2nOUXb9+HWlpaWjbtq1Cop2vfPnyqFGjBm7fvo3s7GzY29srHO/YseNb7/F/7d1fSJNdHAfwb9pGVrOLNtqii+3CtbCkBdaoLmLFMp3a5cqiWEZ2YTclZVHdBRViVASBmGNYCELYhg0shjWGEjaCQWYtpelMqKw5/6TOvRey5dz0bb0+71uv38+d5zw7Z8/vufk9x985m62wsBBNTU2w2WwoKCiItT958gRjY2MoLi5OSKyB6Tp8l8uFQCCAsbExRCKRWL10NP4LTYiYE9Hvjck2ES1Ka9euTdq+cuVKZGZmIhgMIhQKQSKRAADsdjsuXryIkZGROcccHh5OSLbnmgcAPn78iBMnTsy7cXGuzXLJ6sMzMjLm7IvW/86sge7r6wMA+Hy+hA2Osw0ODs7bP3vMZ8+e/e2Y3759S0i254vXXLZv3w6pVAqXyxX3wmOz2QBMJ+MzjY+Po7KyEna7fc4xhdqkKETMiej3xmSbiGiWSCQS93dfXx/OnTsHADh//jx27dqFNWvWxBJFk8kEj8eT8DlgeqVyLhcuXEBnZycMBgOOHz8OlUqFFStWIC0tDQ0NDbh06VLSMQHErbin0jdTdGVeJpNh586d8147+yXi78ZUKpXQarXzXisSiRLa5ovXXNLT07Fv3z5YrVY4HA6YTCZ8+fIFbW1tUCgUceU2AFBXVwe73Q61Wo2KigpkZ2cjMzMTIpEI3d3dyMvLS/k7JJPs2QkRcyL6vTHZJqJFKRAIJG0PhUIYGhrC8uXLY6dXtLa2YmJiAmazGUeOHEn4jN/vT3n+kZERuN1uSKVS3LhxA+np6f94zFTJ5XIA04nfQp2+EV1Vz8rKEvxEj5mKiopgtVphs9lgMpnQ3NyMyclJGI3GhJePlpYWANM102q1Oq4v1bhHXxiS/cejv78/oU2ImBPR740bJIloUfr69SvcbndCe7S0QKvVxpK0YDAI4EeiNNOLFy/w6dOnlOcfGhrC1NQUZDJZQqI9OTkZSwiFJJfLoVKp8ObNm5SSzGiCGQ6HE/pycnIgkUjQ3t4eO9f635CTkwOlUomOjg4EAoHYc5xdQgL8eJ4KhSKh7/HjxynNK5PJACSv8U52VvqvxpyI/lxMtolo0bp27VpcXazf78edO3cAAAcPHoy1K5VKAMCjR4/iVjAHBgZw+fLlX5p79erVkEgkePv2LTo6OmLt4XAY169fF2yD3mwnT55EOBzGqVOn0NXVldD/4cMHNDY2xrVFT2Xp7u5OuF4sFsNsNiMYDKK8vDxWozxTZ2cnmpubF+gOfjAajYhEIrh79y48Hg/UanXSuujo83zw4EFcu8PhQFNTU0pz5ubmAgBqa2sxOjoaa3e73bBYLEk/8ysxJ6I/F8tIiGhR2rx5MyYmJrB3717odDqMj4+jra0No6OjKCoqwp49e2LX6vV6ZGVlwev1wmAwYMuWLfj+/Tva29uh0Wig1Wrh8XhSmn/p0qUoLS1FdXU1Dh8+DJ1Oh1WrVuHVq1f4/PkzSkpKUF9fv9C3naC4uBhdXV2oqanB/v37sWHDBqxbtw6hUAiBQADv37+HRqOJ+3EdvV6Phw8f4vTp09ixY0dsE2n0lzHLysrg8/lgt9uRl5eH7OxsKBQKDA4Owu/3o7e3F7t370Z+fv6C3kthYSFu374dO1Yw2ao2AJSWluL58+eoqqqCw+GASqVCT08PvF4vzGYzamtrf3pOo9GImpoaeDwe5OfnY9OmTejv74fX68XRo0eTjvUrMSeiPxdXtoloURKLxbBYLCgoKIDH44HL5YJcLsfZs2cTamnFYjHq6+tx4MABiMViOJ1O+Hw+HDp0CPfu3Uu60e9nlJWV4erVq1i/fj1evnwJt9sNjUaDhoYGbNy4cSFu86dUVFSgrq4Oer0eAwMDePr0KV6/fo2MjAwcO3YMV65cibveYDCgsrIScrkcTqcTjY2NcSuxaWlpqKqqws2bN7Ft2zb09PSgpaUF7969g1QqRXl5Oc6cObPg96FUKmPHLC5ZsgRGozHpdbm5ubh//z50Oh16e3vhdDohEolw69YtlJSUpDTnsmXLYLFYYDQaMTw8jNbWVkxNTaG6unresVKNORH9uZZE5trqTkT0PxRdVd26dSusVut//XWIiOh/jivbREREREQCYbJNRERERCQQJttERERERAJhzTYRERERkUC4sk1EREREJBAm20REREREAmGyTUREREQkECbbREREREQCYbJNRERERCQQJttERERERAJhsk1EREREJBAm20REREREAvkL2YhcjSDXobAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(-3.2, 3.2, 100)\n",
+    "\n",
+    "plt.plot(x, stats.norm.pdf(x, 0, 1), ls=\"dashed\", color=\"k\")\n",
+    "plt.plot(x, stats.norm.pdf(x, 0, 0.5), lw=1, color=\"k\")\n",
+    "plt.plot(x, stats.norm.pdf(x, 0, 0.2), lw=3, color=\"k\")\n",
+    "plt.xlabel(\"parameter value\")\n",
+    "plt.ylabel(\"Density\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1492,7 +5656,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1501,7 +5665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1510,9 +5674,45 @@
      "text": [
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [sigma, b, a]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 30000/30000 [00:27<00:00, 1098.57draws/s]\n"
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [sigma, b, a]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='60000' class='' max='60000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [60000/60000 05:55<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 10_000 tune and 5_000 draw iterations (40_000 + 20_000 draws total) took 401 seconds.\n",
+      "The acceptance probability does not match the target. It is 0.6766816196175297, but should be close to 0.8. Try to increase the number of tuning steps.\n"
      ]
     }
    ],
@@ -1535,7 +5735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1558,7 +5758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1577,7 +5777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1595,16 +5795,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "412.2473721341997"
+       "411.710622388941"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1622,16 +5822,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "15.062775341988091"
+       "14.962224194258019"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2240,7 +6440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2253,7 +6453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
@@ -2262,20 +6462,124 @@
      "text": [
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [sigma, bA, a]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1660.43draws/s]\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [sigma, bA, a]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:20<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 72 seconds.\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [sigma, bM, a]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1952.88draws/s]\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [sigma, bM, a]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:20<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 70 seconds.\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [sigma, bM, bA, a]\n",
-      "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:01<00:00, 1450.27draws/s]\n",
-      "The acceptance probability does not match the target. It is 0.884650266255495, but should be close to 0.8. Try to increase the number of tuning steps.\n"
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [sigma, bM, bA, a]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:32<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 84 seconds.\n"
      ]
     }
    ],
@@ -2324,15 +6628,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/oscar/miniconda3/envs/py3/lib/python3.7/site-packages/arviz/stats/stats.py:532: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n",
-      "  \"Estimated shape parameter of Pareto distribution is greater than 0.7 for \"\n"
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n"
      ]
     },
     {
@@ -2371,36 +6679,36 @@
        "    <tr>\n",
        "      <th>m_5_1</th>\n",
        "      <td>0</td>\n",
-       "      <td>126.508</td>\n",
-       "      <td>3.41868</td>\n",
+       "      <td>126.665</td>\n",
+       "      <td>3.58363</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.691599</td>\n",
-       "      <td>12.3723</td>\n",
+       "      <td>0.67007</td>\n",
+       "      <td>12.323</td>\n",
        "      <td>0</td>\n",
-       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "      <td>deviance</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>m_5_3</th>\n",
        "      <td>1</td>\n",
-       "      <td>128.869</td>\n",
-       "      <td>4.88707</td>\n",
-       "      <td>2.36092</td>\n",
-       "      <td>0.235037</td>\n",
-       "      <td>10.1269</td>\n",
-       "      <td>0.875712</td>\n",
+       "      <td>128.703</td>\n",
+       "      <td>4.78848</td>\n",
+       "      <td>2.03761</td>\n",
+       "      <td>0.257664</td>\n",
+       "      <td>10.106</td>\n",
+       "      <td>0.784586</td>\n",
        "      <td>False</td>\n",
        "      <td>deviance</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>m_5_2</th>\n",
        "      <td>2</td>\n",
-       "      <td>140.334</td>\n",
-       "      <td>2.97358</td>\n",
-       "      <td>13.8261</td>\n",
-       "      <td>0.0733639</td>\n",
-       "      <td>12.8529</td>\n",
-       "      <td>8.82372</td>\n",
+       "      <td>140.576</td>\n",
+       "      <td>3.07717</td>\n",
+       "      <td>13.9111</td>\n",
+       "      <td>0.0722663</td>\n",
+       "      <td>12.4061</td>\n",
+       "      <td>9.14968</td>\n",
        "      <td>False</td>\n",
        "      <td>deviance</td>\n",
        "    </tr>\n",
@@ -2410,9 +6718,9 @@
       ],
       "text/plain": [
        "      rank      loo    p_loo    d_loo     weight       se       dse warning  \\\n",
-       "m_5_1    0  126.508  3.41868        0   0.691599  12.3723         0    True   \n",
-       "m_5_3    1  128.869  4.88707  2.36092   0.235037  10.1269  0.875712   False   \n",
-       "m_5_2    2  140.334  2.97358  13.8261  0.0733639  12.8529   8.82372   False   \n",
+       "m_5_1    0  126.665  3.58363        0    0.67007   12.323         0   False   \n",
+       "m_5_3    1  128.703  4.78848  2.03761   0.257664   10.106  0.784586   False   \n",
+       "m_5_2    2  140.576  3.07717  13.9111  0.0722663  12.4061   9.14968   False   \n",
        "\n",
        "      loo_scale  \n",
        "m_5_1  deviance  \n",
@@ -2420,7 +6728,7 @@
        "m_5_2  deviance  "
       ]
      },
-     "execution_count": 44,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2440,23 +6748,151 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.argpartition?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>&lt;xarray.DataArray &#x27;waic_i&#x27; (D_dim_0: 2)&gt;\n",
+       "array([19, 12], dtype=int64)\n",
+       "Coordinates:\n",
+       "  * D_dim_0  (D_dim_0) int32 48 49</pre>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'waic_i' (D_dim_0: 2)>\n",
+       "array([19, 12], dtype=int64)\n",
+       "Coordinates:\n",
+       "  * D_dim_0  (D_dim_0) int32 48 49"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argpartition(waic_m_5_3.waic_i, -3)[-2:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>&lt;xarray.DataArray &#x27;waic_i&#x27; (D_dim_0: 2)&gt;\n",
+       "array([12.36362774,  7.66574049])\n",
+       "Coordinates:\n",
+       "  * D_dim_0  (D_dim_0) int32 12 19</pre>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'waic_i' (D_dim_0: 2)>\n",
+       "array([12.36362774,  7.66574049])\n",
+       "Coordinates:\n",
+       "  * D_dim_0  (D_dim_0) int32 12 19"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "waic_m_5_3.waic_i[np.array(np.argsort(-waic_m_5_3.waic_i)[:2])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>&lt;xarray.DataArray &#x27;waic_i&#x27; (D_dim_0: 50)&gt;\n",
+       "array([ 4.07271539,  3.97220008,  1.85230216,  4.4724078 ,  2.01525883,\n",
+       "        2.8586574 ,  2.89407965,  1.56752033,  1.79892544,  1.95302395,\n",
+       "        2.88681812,  1.75174749, 12.36362774,  1.89081726,  1.93857213,\n",
+       "        1.50152486,  1.51060976,  3.12464993,  2.13353072,  7.66574049,\n",
+       "        1.52834387,  1.58709069,  1.55730666,  3.94698096,  2.11065381,\n",
+       "        1.7059347 ,  1.93586336,  2.54687892,  1.94207956,  4.05481098,\n",
+       "        1.5336865 ,  2.16798003,  1.50195821,  3.90255157,  1.51618567,\n",
+       "        2.97508792,  1.67492613,  2.19578799,  2.60065866,  2.33066607,\n",
+       "        1.79260136,  1.91840872,  1.58386615,  3.09917039,  1.65111233,\n",
+       "        1.60158873,  1.51741493,  1.55988902,  2.24761202,  1.95124587])\n",
+       "Coordinates:\n",
+       "  * D_dim_0  (D_dim_0) int32 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49</pre>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'waic_i' (D_dim_0: 50)>\n",
+       "array([ 4.07271539,  3.97220008,  1.85230216,  4.4724078 ,  2.01525883,\n",
+       "        2.8586574 ,  2.89407965,  1.56752033,  1.79892544,  1.95302395,\n",
+       "        2.88681812,  1.75174749, 12.36362774,  1.89081726,  1.93857213,\n",
+       "        1.50152486,  1.51060976,  3.12464993,  2.13353072,  7.66574049,\n",
+       "        1.52834387,  1.58709069,  1.55730666,  3.94698096,  2.11065381,\n",
+       "        1.7059347 ,  1.93586336,  2.54687892,  1.94207956,  4.05481098,\n",
+       "        1.5336865 ,  2.16798003,  1.50195821,  3.90255157,  1.51618567,\n",
+       "        2.97508792,  1.67492613,  2.19578799,  2.60065866,  2.33066607,\n",
+       "        1.79260136,  1.91840872,  1.58386615,  3.09917039,  1.65111233,\n",
+       "        1.60158873,  1.51741493,  1.55988902,  2.24761202,  1.95124587])\n",
+       "Coordinates:\n",
+       "  * D_dim_0  (D_dim_0) int32 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "waic_m_5_3.waic_i  # .max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/oscar/miniconda3/envs/py3/lib/python3.7/site-packages/arviz/stats/stats.py:1210: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\data\\io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
+      "  FutureWarning,\n",
+      "C:\\Users\\janka.WISMAIN\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\arviz\\stats\\stats.py:1415: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n",
       "See http://arxiv.org/abs/1507.04544 for details\n",
       "  \"For one or more samples the posterior variance of the log predictive \"\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 720x480 with 1 Axes>"
+       "Text(0.5, 1.0, 'Gaussian model (m5.3)')"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -2467,20 +6903,49 @@
     "psis_m_5_3 = az.loo(m_5_3_trace, pointwise=True, scale=\"deviance\")\n",
     "waic_m_5_3 = az.waic(m_5_3_trace, pointwise=True, scale=\"deviance\")\n",
     "\n",
+    "# get indices of max WAIC to label the points\n",
+    "label_max = np.array(np.argsort(-waic_m_5_3.waic_i)[:2])\n",
+    "\n",
     "# Figure 7.10\n",
-    "plt.scatter(psis_m_5_3.pareto_k, waic_m_5_3.waic_i)\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.scatter(psis_m_5_3.pareto_k, waic_m_5_3.waic_i, edgecolor=\"b\", color=(0, 0, 0, 0))\n",
+    "plt.scatter(psis_m_5_3.pareto_k[label_max[0]], waic_m_5_3.waic_i[label_max[0]], c=\"k\")\n",
+    "plt.axvline(0.5, linestyle=\"--\", color=\"k\")\n",
+    "plt.text(\n",
+    "    psis_m_5_3.pareto_k[label_max[0]] - 0.04,\n",
+    "    waic_m_5_3.waic_i[label_max[0]],\n",
+    "    \"ID\",\n",
+    "    va=\"center\",\n",
+    ")\n",
+    "plt.text(\n",
+    "    psis_m_5_3.pareto_k[label_max[1]] - 0.045,\n",
+    "    waic_m_5_3.waic_i[label_max[1]],\n",
+    "    \"ME\",\n",
+    "    va=\"center\",\n",
+    ")\n",
     "plt.xlabel(\"PSIS Pareto k\")\n",
-    "plt.ylabel(\"WAIC\");"
+    "plt.ylabel(\"WAIC penalty\")\n",
+    "plt.title(\"Gaussian model (m5.3)\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "text/plain": [
+       "Text(2, 2, 'Student-t')"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 800x350 with 2 Axes>"
       ]
@@ -2500,10 +6965,14 @@
     "fig, (ax, lax) = plt.subplots(1, 2, figsize=[8, 3.5])\n",
     "\n",
     "ax.plot(v, g.pdf(v), color=\"b\")\n",
+    "ax.text(-3, 0.2, \"Gaussian\", color=\"b\")\n",
     "ax.plot(v, t.pdf(v), color=\"k\")\n",
+    "ax.text(1, 0.3, \"Student-t\", color=\"k\")\n",
     "\n",
     "lax.plot(v, -g.logpdf(v), color=\"b\")\n",
-    "lax.plot(v, -t.logpdf(v), color=\"k\");"
+    "lax.text(-2, 4, \"Gaussian\", color=\"b\")\n",
+    "lax.plot(v, -t.logpdf(v), color=\"k\")\n",
+    "lax.text(2, 2, \"Student-t\", color=\"k\")"
    ]
   },
   {
@@ -2600,7 +7069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
@@ -2608,14 +7077,14 @@
      "output_type": "stream",
      "text": [
       "statsmodels.api 0.11.1\n",
-      "arviz           0.7.0\n",
-      "numpy           1.18.1\n",
-      "pymc3           3.8\n",
-      "pandas          1.0.3\n",
-      "last updated: Tue May 19 2020 \n",
+      "arviz           0.9.0\n",
+      "pandas          1.1.0\n",
+      "pymc3           3.9.2\n",
+      "numpy           1.16.2\n",
+      "last updated: Tue Oct 06 2020 \n",
       "\n",
-      "CPython 3.7.6\n",
-      "IPython 7.14.0\n",
+      "CPython 3.7.3\n",
+      "IPython 7.17.0\n",
       "watermark 2.0.2\n"
      ]
     }
@@ -2642,7 +7111,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,