Inference algorithms in Bayesian networks and the Probanet system

被引：5

作者：

Pan, HP ^{[1
]}

McMichael, D ^{[1
]}

Lendjel, M ^{[1
]}

机构：

[1] Cooperat Res Ctr Sensor Signal & Informat Proc, The Levels, SA, Australia

来源：

DIGITAL SIGNAL PROCESSING | 1998年 / 8卷 / 04期

关键词：

D O I：

10.1006/dspr.1998.0324

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper reviews and formalizes algorithms for probabilistic inferences upon causal probabilistic networks (CPN), also known as Bayesian networks, and introduces Probanet-a development environment for CPNs. Information fusion in CPNs is realized through updating joint probabilities of the variables upon the arrival of new evidences or new hypotheses. Kernel algorithms for some dominant methods of inferences are formalized from discontiguous, mathematics-oriented literatures, with gaps filled in with regards to computability and completeness. Probanet has been designed and developed as a generic shell, a development environment for CPN construction and application. The design aspects and current status of Probanet are described. (C) 1998 Academic Press.

引用

页码：231 / 243

页数：13