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Search-based methods to bound diagnostic probabilities in very large belief nets
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An Introduction to Algorithms for Inference in Belief Nets
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Pruning bayesian networks for efficient computation
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Probabilistic Horn abduction and Bayesian networks
Probabilistic Horn abduction and Bayesian networks
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This paper discusses how conflicts (as used by the consistency-based diagnosis community) can be adapted to be used in a search-based algorithm for computing prior and posterior probabilities in discrete Bayesian Networks. This is an "anytime" algorithm, that at any stage can estimate the probabilities and give an error bound. Whereas the most popular Bayesian net algorithms exploit the structure of the network for efficiency, we exploit probability distributions for efficiency; this algorithm is most suited to the case with extreme probabilities. This paper presents a solution to the inefficiencies found in naive algorithms, and shows how the tools of the consistency-based diagnosis comniunity (namely conflicts) can be used effectively to improve the efficiency. Empirical results with networks having tens of thousands of nodes are presented.