Encoding probability propagation in belief networks

被引：2

作者：

Zhang, SC ^{[1
]}

Zhang, CQ

机构：

[1] Univ Technol Sydney, Fac Informat Technol, Sydney, NSW 2007, Australia

[2] Guangxi Normal Univ, Sch Math & Comp, Guilin, Peoples R China

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS | 2002年 / 32卷 / 04期

关键词：

approximating reasoning; Bayesian network; belief network; encoding technology; probabilistic reasoning;

D O I：

10.1109/TSMCA.2002.804784

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Complexity reduction is an important task in Bayesian networks. Recently, an approach known as the linear potential function (LPF) model has been proposed for approximating Bayesian computations. The LPF model can effectively compress a conditional probability table into a linear function. This correspondence extends the LPF model to approximate propagation in Bayesian networks. The extension focuses on encoding probability propagation as a polynomial function for a class of tractable problems.

引用

页码：526 / 531

页数：6

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