An efficient algorithm for finding the M most probable configurations in probabilistic expert systems

被引：88
|
作者：
Nilsson, D [1 ]
机构：
[1] Univ Aalborg, Dept Math & Comp Sci, Inst Elect Syst, DK-9220 Aalborg Ost, Denmark
来源：
STATISTICS AND COMPUTING | 1998年 / 8卷 / 02期
关键词：
Bayesian network; belief revision; charger; conditional independence; divide-and-conquer; evidence; flow; junction tree; marginalization; maximization; most probable explanation; potential function; propagation;
D O I：
10.1023/A:1008990218483
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
A probabilistic expert system provides a graphical representation of a joint probability distribution which enables local computations of probabilities. Dawid (1992) provided a 'flow-propagation' algorithm for finding the most probable configuration of the joint distribution in such a system. This paper analyses that algorithm in detail, and shows how it can be combined with a clever partitioning scheme to formulate an efficient method for finding the M most probable configurations. The algorithm is a divide and conquer technique. that iteratively identifies the M most probable configurations.
引用
收藏
页码：159 / 173
页数：15
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