Constructing probabilistic models

Radim Jiroušek

doi:10.1016/S1386-5056(97)00026-9

1997-06-01

PRE

Radim Jiroušek *

Nicholas Kushmerick

DOI：10.1016/S1386-5056(97)00026-9

原文链接

摘要

En 中文

Bayesian networks have become one of the most popular probabilistic techniques in AI, largely due to the development of several efficient inference algorithms. In this paper we describe a heuristic method for constructing Bayesian networks. Our construction method relies on the relationship between Bayesian networks and decomposable models, a special kind of graphical model. We explain this relationship and then show how it can be used to facilitate model construction. Finally, we describe an implemented computer program that illustrates these ideas. (C) 1997 Elsevier Science Ireland Ltd.

Keyword:

uncertainty

probability theory

graph models

Bayesian network

learning

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对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

International Journal of Medical Informatics

IF:

4.1

论文数:

4.6K

被引数:

1.1W

机构

暂无机构信息

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