Uncertainty in Artificial Intelligence
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Iterative Join-Graph Propagation
Rina Dechter, Kalev Kask, Robert Mateescu
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 as an iterative approximation scheme on one hand, and by a recently introduced mini-clustering 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.
Keywords:
Pages: 128-136
PS Link:
PDF Link: /papers/02/p128-dechter.pdf
BibTex:
@INPROCEEDINGS{Dechter02,
AUTHOR = "Rina Dechter and Kalev Kask and Robert Mateescu",
TITLE = "Iterative Join-Graph Propagation",
BOOKTITLE = "Proceedings of the Eighteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-02)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2002",
PAGES = "128--136"
}


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