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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NILSSON, D. An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems. Statistics and Computing 8, 159–173 (1998). https://doi.org/10.1023/A:1008990218483
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DOI: https://doi.org/10.1023/A:1008990218483