Theoretical analysis of accuracy of Gaussian belief propagation

被引：0
|
作者：
Nishiyama, Yu [1 ]
Watanabe, Sumio [2 ]
机构：
[1] Tokyo Inst Technol, Dept Computat Intelligence & Syst Sci, Midori Ku, 4259 Nagatuta, Yokohama, Kanagawa 2268503, Japan
[2] Tokyo Inst Technol, Precis & Intelligence Lab, Yokosuka, Kanagawa 2268503, Japan
来源：
ARTIFICIAL NEURAL NETWORKS - ICANN 2007, PT 1, PROCEEDINGS | 2007年 / 4668卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
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页码：29 / +
页数：2
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