Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms

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Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms (2005)

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by Jonathan S. Yedidia , William T. Freeman , Yair Weiss

Venue:	IEEE Transactions on Information Theory
Citations:	585 - 13 self

BibTeX

@ARTICLE{Yedidia05constructingfree,
    author = {Jonathan S. Yedidia and William T. Freeman and Yair Weiss},
    title = {Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms},
    journal = {IEEE Transactions on Information Theory},
    year = {2005},
    volume = {51},
    pages = {2282--2312}
}

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Abstract

Important inference problems in statistical physics, computer vision, error-correcting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems that is exact when the factor graph is a tree, but only approximate when the factor graph has cycles. We show that BP fixed points correspond to the stationary points of the Bethe approximation of the free energy for a factor graph. We explain how to obtain regionbased free energy approximations that improve the Bethe approximation, and corresponding generalized belief propagation (GBP) algorithms. We emphasize the conditions a free energy approximation must satisfy in order to be a “valid ” or “maxent-normal ” approximation. We describe the relationship between four different methods that can be used to generate valid approximations: the “Bethe method, ” the “junction graph method, ” the “cluster variation method, ” and the “region graph method.” Finally, we explain how to tell whether a region-based approximation, and its corresponding GBP algorithm, is likely to be accurate, and describe empirical results showing that GBP can significantly outperform BP.

Keyphrases

free energy approximation factor graph generalized belief propagation algorithm bethe approximation artificial intelligence region-based approximation point correspond statistical physic region graph method important inference problem efficient way stationary point regionbased free energy approximation free energy empirical result error-correcting coding theory maxent-normal approximation bethe method corresponding gbp algorithm belief propagation cluster variation method generalized belief propagation different method junction graph method valid approximation computer vision marginal probability

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