A formal comparison of variable elimination and arc reversal in Bayesian network inference

被引：6

作者：

Butz, C. J. ^{[1
]}

Chen, J. ^{[1
]}

Konkel, K. ^{[1
]}

Lingras, P. ^{[2
]}

机构：

[1] Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada

[2] St Marys Univ, Dept Math & Comp Sci, Halifax, NS B3H 3C3, Canada

来源：

INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS | 2009年 / 3卷 / 03期

关键词：

D O I：

10.3233/IDT-2009-0064

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];

摘要：

We present a comparative study of two approaches to Bayesian network inference, called variable elimination (VE) and arc reversal (AR). It is established that VE never requires more space than AR, and never requires more computation (multiplications and additions) than AR. These two characteristics are supported by experimental results on six large BNs, which indicate that VE is never slower than AR and can perform inference significantly faster than AR.

引用

页码：173 / 180

页数：8