Uncertainty in Artificial Intelligence
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Convergent Message-Passing Algorithms for Inference over General Graphs with Convex Free Energies
Tamir Hazan, Amnon Shashua
Abstract:
Inference problems in graphical models can be represented as a constrained optimization of a free energy function. It is known that when the Bethe free energy is used, the fixedpoints of the belief propagation (BP) algorithm correspond to the local minima of the free energy. However BP fails to converge in many cases of interest. Moreover, the Bethe free energy is non-convex for graphical models with cycles thus introducing great difficulty in deriving efficient algorithms for finding local minima of the free energy for general graphs. In this paper we introduce two efficient BP-like algorithms, one sequential and the other parallel, that are guaranteed to converge to the global minimum, for any graph, over the class of energies known as "convex free energies". In addition, we propose an efficient heuristic for setting the parameters of the convex free energy based on the structure of the graph.
Keywords:
Pages: 264-273
PS Link:
PDF Link: /papers/08/p264-hazan.pdf
BibTex:
@INPROCEEDINGS{Hazan08,
AUTHOR = "Tamir Hazan and Amnon Shashua",
TITLE = "Convergent Message-Passing Algorithms for Inference over General Graphs with Convex Free Energies",
BOOKTITLE = "Proceedings of the Twenty-Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-08)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2008",
PAGES = "264--273"
}


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