Efficient search-based inference for noisy-OR belief networks: topepsilon

Authors:
Kurt Huang;Max Henrion
Affiliations:
Section on Medical Informatics, Stanford, CA;Institute for Decision Systems Research, Los Altos, CA
Venue:
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Year:
1996

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Abstract

Inference algorithms for arbitrary belief networks are impractical for large, complex belief networks. Inference algorithms for specialized classes of belief networks have been shown to be more efficient. In this paper, we present a search-based algorithm for approximate inference on arbitrary, noisy-OR belief networks, generalizing earlier work on search-based inference for two-level, noisy-OR belief networks. Initial experimental results appear promising.