Correctness of belief propagation in gaussian graphical models of arbitrary topology (2001)
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| Venue: | Neural Computation |
| Citations: | 176 - 8 self |
BibTeX
@ARTICLE{Weiss01correctnessof,
author = {Yair Weiss and William T. Freeman},
title = {Correctness of belief propagation in gaussian graphical models of arbitrary topology},
journal = {Neural Computation},
year = {2001},
volume = {13},
pages = {2173--2200}
}
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Abstract
Graphical models, such asBayesian networks and Markov Random Fields represent statistical dependencies of variables by a graph. Local \belief propagation " rules of the sort proposed by Pearl [18] are guaranteed to converge to the correct posterior probabilities in singly connected graphical models. Recently, anumber of researchers have empirically demonstrated good performance of \loopy belief propagation"{using these same rules on graphs with loops. Perhaps the most dramatic instance is the near Shannonlimit performance of \Turbo codes", whose decoding algorithm is equivalent toloopy belief propagation. Except for the case of graphs with a single loop, there has been little theoretical understanding of the performance of loopy propagation. Here we analyze belief propagation in networks with arbitrary topologies when the nodes in the graph describe jointly Gaussian
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