Iterative join-graph propagation

Authors:
Rina Dechter;Kalev Kask;Robert Mateescu
Affiliations:
Department of Information and Computer Science, University of California, Irvine, CA;Department of Information and Computer Science, University of California, Irvine, CA;Department of Information and Computer Science, University of California, Irvine, CA
Venue:
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Year:
2002

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Abstract

The paper presents an iterative version of join-tree clustering that applies the message passing of join-tree clustering algorithm to join-graphs rather than to join-trees, iteratively. It is inspired by the success of Pearl's belief propagation algorithm (BP) as an iterative approximation scheme on one hand, and by a recently introduced mini-clustering (MC(i)) success as an anytime approximation method, on the other. The proposed Iterative Join-graph Propagation (IJGP) belongs to the class of generalized belief propagation methods, recently proposed using analogy with algorithms in statistical physics. Empirical evaluation of this approach on a number of problem classes demonstrates that even the most time-efficient variant is almost always superior to IBP and MC(i), and is sometimes more accurate by as much as several orders of magnitude.