Approximating Bayesian belief networks by arc removal

被引：25

作者：

vanEngelen, RA

机构：

[1] Department of Computer Science, Leiden University, PB 9512

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1997年 / 19卷 / 08期

关键词：

Bayesian belief networks; belief network approximation; model simplification; approximate probabilistic inference; information theory;

D O I：

10.1109/34.608295

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

I propose a general framework for approximating Bayesian belief networks through model simplification by are removal. Given an upper bound on the absolute error allowed on the prior and posterior probability distributions of the approximated network, a subset of arcs is removed, thereby speeding up probabilistic inference.

引用

页码：916 / 920

页数：5

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