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Author Wenhui Liao ♦ Weihong Zhang ♦ Qiang Ji
Sponsorship IEEE Comput. Soc.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2004
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Inference algorithms ♦ Bayesian methods ♦ Partitioning algorithms ♦ Computer networks ♦ Application software ♦ Systems engineering and theory ♦ Tree graphs ♦ Computational efficiency ♦ Distributed computing ♦ NP-hard problem
Abstract In a Bayesian network, a probabilistic inference is the procedure of computing the posterior probability of query variables given a collection of evidences. In This work, we propose an algorithm that efficiently carries out the inferences whose query variables and evidence variables are restricted to a subset of the set of the variables in a BN. The algorithm successfully combines the advantages of two popular inference algorithms - variable elimination and clique tree propagation. We empirically demonstrate its computational efficiency in an affective computing domain.
Description Author affiliation: Dept. of Electr., Comput. & Syst. Eng., Rensselaer Polytech. Inst., Troy, NY, USA (Wenhui Liao; Weihong Zhang; Qiang Ji)
ISBN 076952236X
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 2004-11-15
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 167.23 kB
Page Count 5
Starting Page 652
Ending Page 656

Source: IEEE Xplore Digital Library