(Enter summary)
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 approximate when the factor
graph has cycles. We show that BP fixed points correspond to the stationary points of the
Bethe approximation... (Update)
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BibTeX entry: (Update)
J.S. YEDIDIA, W.T. FREEMAN, AND Y. WEISS, "Constructing free energy ap- proximations and generalized belief propagation algorithms," available at www.marl. com/papars/TI2002-35/ http://citeseer.ist.psu.edu/yedidia04constructing.html More
@misc{ yedidia-constructing,
author = "J. YEDIDIA and W. FREEMAN and Y. WEISS",
title = "Constructing free energy ap- proximations and generalized belief propagation
algorithms",
text = "J.S. YEDIDIA, W.T. FREEMAN, AND Y. WEISS, Constructing free energy ap-
proximations and generalized belief propagation algorithms, available at
www.marl. com/papars/TI2002-35/",
url = "citeseer.ist.psu.edu/yedidia04constructing.html" }
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