Belief-propagation-guided Monte-Carlo sampling

Abstract

A Monte-Carlo algorithm for discrete statistical models that combines the full power of the Belief Propagation algorithm with the advantages of a detailed-balanced heat bath approach is presented. A sub-tree inside the factor graph is first extracted randomly; Belief Propagation is then used as a perfect sampler to generate a configuration on the tree given the boundary conditions and the procedure is iterated. This appoach is best adapted for locally tree like graphs, it is therefore tested on the hard cases of spin-glass models for random graphs demonstrating its state-of-the art status in those cases.