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Belief Propagation (BP) is one of the most popular methods for inference inprobabilistic graphical models. BP is guaranteed to return the correct answerfor tree structures, but can be incorrect or non-convergent for loopy graphicalmodels. Recently, several new approximate inference algorithms based on cavitydistribution have been proposed. These methods can account for the effect ofloops by incorporating the dependency between BP messages. Alternatively,region-based approximations (that lead to methods such as Generalized BeliefPropagation) improve upon BP by considering interactions within small clustersof variables, thus taking small loops within these clusters into account. Thispaper introduces an approach, Generalized Loop Correction (GLC), that benefitsfrom both of these types of loop correction. We show how GLC relates to thesetwo families of inference methods, then provide empirical evidence that GLCworks effectively in general, and can be significantly more accurate than bothcorrection schemes.

A Generalized Loop Correction Method for Approximate Inference in Graphical Models