An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems |
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| Authors: | D NILSSON |
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| Institution: | (1) Department of Mathematics and Computer Science, Institute for Electronic Systems, Aalborg University, Fredrik Bajers Vej 7 E, 9220 Aalborg Øst, Denmark |
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| Abstract: | A probabilistic expert system provides a graphical representation of a joint probability distribution which enables local computations of probabilities. Dawid (1992) provided a  flow- propagation algorithm for finding the most probable configuration of the joint distribution in such a system. This paper analyses that algorithm in detail, and shows how it can be combined with a clever partitioning scheme to formulate an efficient method for finding the M most probable configurations. The algorithm is a divide and conquer technique, that iteratively identifies the M most probable configurations. |
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| Keywords: | Bayesian network belief revision charge conditional independence divide-and-conquer evidence flow junction tree marginalization maximization most probable explanation potential function propagation |
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