Optimizing Bayesian belief networks: A case study of information retrieval systems

M. T. Indrawan, B. Srinivasan, C. C. Wilson, R. Redpath

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3 Citations (Scopus)


Bayesian Belief networks have been used widely to solve many decision problem that involve uncertainty. One major advantage of this approach compared with other reasoning tools is its semantic richness in describing the decision process. Some inference algorithms for carrying out the reasoning process exist, such as those of Pearl and of Lauritzen, however, these algorithms are known to be computationally expensive. Hence, they require optimization to make them practical. This paper proposes two optimization techniques for Bayesian Belief networks. These optimization techniques were investigated for Information Retrieval applications, but can also be applied to different applications outside the information retrieval area.

Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Systems, Man and Cybernetics
PublisherIEEE Computer Society
Number of pages6
Publication statusPublished - 1 Dec 1998
EventIEEE International Conference on Systems, Man and Cybernetics 1998 - San Diego, United States of America
Duration: 11 Oct 199814 Oct 1998
https://ieeexplore.ieee.org/xpl/conhome/5875/proceeding?isnumber=15656 (Proceedings)


ConferenceIEEE International Conference on Systems, Man and Cybernetics 1998
Abbreviated titleSMC 1998
Country/TerritoryUnited States of America
CitySan Diego
Internet address

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