Hill-climbing and branch-and-bound algorithms for exact and approximate inference in credal networks

被引：19

作者：

Cano, Andres ^{[1
]}

Gomez, Manuel ^{[1
]}

Moral, Serafin ^{[1
]}

Abellan, Joaquin ^{[1
]}

机构：

[1] Univ Granada, ETS Ingn Informat, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, Spain

来源：

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING | 2007年 / 44卷 / 03期

关键词：

credal network; probability intervals; Bayesian networks; strong independence; hill-climbing; branch-and-bound algorithms;

D O I：

10.1016/j.ijar.2006.07.020

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes two new algorithms for inference in credal networks. These algorithms enable probability intervals to be obtained for the states of a given query variable. The first algorithm is approximate and uses the hill-climbing technique in the Shenoy-Shafer architecture to propagate in join trees; the second is exact and is a modification of Rocha and Cozman's branch-and-bound algorithm, but applied to general directed acyclic graphs, (C) 2006 Elsevier Inc. All rights reserved.

引用

页码：261 / 280

页数：20

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