Approaches to Bayesian Network Model Construction

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原文链接

NETL

外文摘要：Bayesian Network (BN) has sound mathematical basis， enables reasoning under uncertainty， and facilitates the update of beliefs， given new evidence。 It also enables the visual representation of a model。 These make BN suitable for solving uncertainty problems。 This chapter details BN model construction approaches and presents our experiences with selecting the optimal construction approach。

外文关键词：

Bayesian networksBN model constructionBN model parameterizationParameter learningPerformance indexStructure learning

作者：

Ifeyinwa E. Achumba、Djamel Azzi、Ifeanyi Ezebili、Sebastian Bersch

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作者单位：

Faculty of Technology, School of Engineering, University of Portsmouth, Anglesea Building, Anglesea Road, Portsmouth, PO1 3DJ, UK

Department of Electrical and Electronic Engineering, School of Engineering and Engineering Technology, Federal University of Technology, Owerri, Owerri PMB 1526, Imo State, Nigeria

会议名称：

International conference on advances in engineering technologies and physical science;World congress on engineering

会议地点：

London(GB)

会议母体文献：

IAENG transactions on engineering technologies

页码：

461-474

出版时间：

2012

DOI：

10.1007/978-94-007-6190-2_35