# MCMC assisted by Belief Propagaion

@article{Ahn2016MCMCAB, title={MCMC assisted by Belief Propagaion}, author={Sungsoo Ahn and Michael Chertkov and Jinwoo Shin}, journal={ArXiv}, year={2016}, volume={abs/1605.09042} }

Markov Chain Monte Carlo (MCMC) and Belief Propagation (BP) are the most popular algorithms for computational inference in Graphical Models (GM). In principle, MCMC is an exact probabilistic method which, however, often suffers from exponentially slow mixing. In contrast, BP is a deterministic method, which is typically fast, empirically very successful, however in general lacking control of accuracy over loopy graphs. In this paper, we introduce MCMC algorithms correcting the approximation…

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