Presentation | 2007-05-21 Theoretical Analysis of Accuracy of Belief Propagation in Gaussian Models Yu NISHIYAMA, Sumio WATANABE, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Belief propagation (BP) is and algorithm which can compute marginal probability distributions with a tractable computational cost. Loopy belief propagation (LBP) applied to the graphs containing loops is known to provide marginal distributions approximately if LBP converges. In this paper, we apply LBP to a multi-dimensional Gaussian distribution that has loops and analytically show how accurate LBP is in some cases. Specifically, we analytically show messages, approximate marginal densities, and the KL distance at fixed points of LBP when the graph corresponding to a Gaussian distribution has at most a single loop. Basides, for the graphs which have arbitrary structures, we derive the expansions of approximate marginal densities when covariances are small. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Loopy Belief Propagation / Kullback Leibler Distance / Single Loop |
Paper # | NC2007-6 |
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Committee | NC |
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Conference Date | 2007/5/14(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Neurocomputing (NC) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Theoretical Analysis of Accuracy of Belief Propagation in Gaussian Models |
Sub Title (in English) | |
Keyword(1) | Loopy Belief Propagation |
Keyword(2) | Kullback Leibler Distance |
Keyword(3) | Single Loop |
1st Author's Name | Yu NISHIYAMA |
1st Author's Affiliation | Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology() |
2nd Author's Name | Sumio WATANABE |
2nd Author's Affiliation | Precision and Intelligence Laboratory, Tokyo Institute of Technology |
Date | 2007-05-21 |
Paper # | NC2007-6 |
Volume (vol) | vol.107 |
Number (no) | 50 |
Page | pp.pp.- |
#Pages | 6 |
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