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Author Rong Pan ♦ Yun Peng ♦ Zhongli Ding
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2006
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Bayesian methods ♦ Probability distribution ♦ Iterative methods ♦ Inference algorithms ♦ Iterative algorithms ♦ Computer science ♦ Equations ♦ Engines
Abstract This paper reports our investigation on the problem of belief update in Bayesian networks (BN) using uncertain evidence. We focus on two types of uncertain evidences, virtual evidence (represented as likelihood ratios) and soft evidence (represented as probability distributions). We review three existing belief update methods with uncertain evidences: virtual evidence method, Jeffrey's rule, and IPFP (iterative proportional fitting procedure), and analyze the relations between these methods. This in-depth understanding leads us to propose two algorithms for belief update with multiple soft evidences. Both of these algorithms can be seen as integrating the techniques of virtual evidence method, IPFP and traditional BN evidential inference, and they have clear computational and practical advantages over the methods proposed by others in the past
Description Author affiliation: Dept. of Comput. Sci. & Electr. Eng., Maryland Baltimore County Univ., MD (Rong Pan; Yun Peng; Zhongli Ding)
ISBN 0769527280
ISSN 10823409
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2006-11-13
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 223.39 kB
Page Count 4
Starting Page 441
Ending Page 444


Source: IEEE Xplore Digital Library