Thermodynamic Cyclic Processes with Markov Samplers in Bayesian Inference

TMLR Paper8712 Authors

Abstract:

The concept of Markov chain Monte Carlo (MCMC) cycles, an analogy to cyclic processes in
heat engines, is presented in order to examine Bayesian inference problems. In this effort,
we develop adaptive ensemble schedulers that allow the tuning of external parameters of
a Bayesian canonical ensemble during an MCMC run. We apply our method to different
statistical models. As a fundamental insight, we find that such systems produce a non-zero
net work output if and only if the considered model is non-Gaussian.

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Gilles_Louppe1

Submission Number: 8712