PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Constructing Free-Energy Approximations and Generalized Belief Propagation Algorithms
Jonathan Yedidia, Willia Freeman and Yair Weiss
IEEE Transactions on Information Theory Volume 51, Number 7, pp. 2282-2312, 2005.

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 approximateFinally, 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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:1064
Deposited By:Yair Weiss
Deposited On:04 September 2005