Theoretical analysis of accuracy of Gaussian belief propagation

Authors:
Yu Nishiyama;Sumio Watanabe
Affiliations:
Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Japan;Precision and Intelligence Laboratory, Tokyo Institute of Technology, Yokohama, Japan
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

Citing 5
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Abstract

Belief propagation (BP) is the calculation method which enables us to obtain the marginal probabilities with a tractable computational cost. BP is known to provide true marginal probabilities when the graph describing the target distribution has a tree structure, while do approximate marginal probabilities when the graph has loops. The accuracy of loopy belief propagation (LBP) has been studied. In this paper, we focus on applying LBP to a multi-dimensional Gaussian distribution and analytically show how accurate LBP is for some cases.