Theoretical Analysis of Accuracy of Gaussian Belief Propagation

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.

• Type: Conference
• Year: 2007
• Where: ICANN
• Authors: Yu Nishiyama, Sumio Watanabe
• Keywords: Belief Propagation, ICANN 2007, Marginal Probabilities, True Marginal Probabilities