EMAUS (Ensemble Microcanonical Adjusted-Unadjusted Sampler)

[reubenharry]

This is in essence a tuning scheme for running multiple chains in parallel (ensemble). The key trick is to use MCLMC without MH (i.e. unadjusted) first in order to very quickly find the typical set, and then to use MH for the steps you actually count. The details are in how to decide when to switch between these two phases, and how to tune the hyperparameters using estimates from the ensemble of chains.

The upshot is that the method is fast, even compared to other ensemble methods: sampling book: TODO

Changes:

• a new ensemble_mclmc: TODO
• util.py: TODO
• other: TODO

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