Reviews: A-NICE-MC: Adversarial Training for MCMC

This paper describes a novel adversarial training procedure to fit a generative model described by a Markov chain to sampled data. The Markov chain transitions are based on NICE, and so are reversible and volume preserving. It is therefore straightforward to use these as proposals in a Metropolis MCMC method to sample from arbitrary distributions. By repeatedly fitting the Markov chain model to samples from preliminary runs, we can hope that we'll end up with an MCMC method that mixes well on an arbitrary target distribution. Like HMC, the Markov chain is actually on a joint distribution of the parameters of interest, and some auxiliary random draws used to make a deterministic proposal.