Lazy inference in multiply sectioned Bayesian networks using linked junction forests

被引：0

作者：

Xiang, Yang ^{[1
]}

Chen, Xiaoyun ^{[1
]}

机构：

[1] Univ Guelph, Guelph, ON N1G 2W1, Canada

来源：

ADVANCES IN PROBABILISTIC GRAPHICAL MODELS | 2007年 / 213卷

基金：

加拿大自然科学与工程研究理事会;

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：

Lazy propagation reduces the space complexity from HUGIN inference. Multiply Sectioned Bayesian Networks extend Bayesian Networks a cooperative multiagent paradigm. To combine the benefits of the two, a framework was proposed earlier to apply lazy propagation to inference in MSBNs. We propose an alternative framework with a simpler compiled structure. The issues of lazy communication and observation entering in a multiagent setting are considered. We prove that the inference is exact.

引用

页码：175 / +

页数：2

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