Parallelizable Sampling of Markov Random Fields
James Martens, Ilya Sutskever ; JMLR W&CP 9:517-524, 2010.
Markov Random Fields (MRFs) are an important class of probabilistic models which are used for density estimation, classification, denoising, and for constructing Deep Belief Networks. Every application of an MRF requires addressing its inference problem, which can be done using deterministic inference methods or using stochastic Markov Chain Monte Carlo methods. In this paper we introduce a new Markov Chain transition operator that updates all the variables of a pairwise MRF in parallel by using auxiliary Gaussian variables. The proposed MCMC operator is extremely simple to implement and to parallelize. This is achieved by a formal equivalence result between arbitrary pairwise MRFs and a particular type of Restricted Boltzmann Machine. This result also implies that the later can be learned in place of the former without any loss of modeling power, a possibility we explore in experiments.