Statistics and Computing

, Volume 8, Issue 2, pp 159–173 | Cite as

An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems

  • D. NILSSON

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.

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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Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • D. NILSSON
    • 1
  1. 1.Department of Mathematics and Computer Science, Institute for Electronic SystemsAalborg UniversityAalborg ØstDenmark

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