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Methods Inf Med 1991; 30(02): 81-89
DOI: 10.1055/s-0038-1634820
Statistical Applications
Schattauer GmbH

Algorithms for Bayesian Belief-Network Precomputation

E. H. Herskovits
1   Section on Medical Informatics, Stanford University, Stanford CA, USA
,
G. F. Cooper
1   Section on Medical Informatics, Stanford University, Stanford CA, USA
› InstitutsangabenWe thank H. Jacques Suermondt for providing us with his implementation of the Lauritzen-Spiegelhalter algorithm. We gratefully acknowledge the assistance of Ingo Beinlich, who supplied us with the ALARM belief network and provided insightful criticism of earlier versions of this paper. We thank Harold Lehmann for his comments regarding the theoretical basis of our algorithms. Lyn Dupre provided excellent editorial advice. This work is supported by training grant LM-07033 from the National Library of Medicine, by grant IRI-8703710 from the National Science Foundation, and by grant P-25514-EL from the U. S. Army Research office. Computing resources were provided by grant RR-00785 from the Division of Research Resources of the National Institutes of Health.
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Publikationsdatum:
07. Februar 2018 (online)

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Abstract

Bayesian belief networks provide an intuitive and concise means of representing probabilistic relationships among the variables in expert systems. A major drawback to this methodology is its computational complexity. We present an introduction to belief networks, and describe methods for precomputing, or caching, part of a belief network based on metrics of probability and expected utility. These algorithms are examples of a general method for decreasing expected running time for probabilistic inference.

We first present the necessary background, and then present algorithms for producing caches based on metrics of expected probability and expected utility. We show how these algorithms can be applied to a moderately complex belief network, and present directions for future research.

Key-Words

Expert Systems - Algorithms - Computer-Assisted Diagnosis - Probability Theory

1 A belief network is a special case of an influence diagram. An influence diagram represents decision alternatives and outcome values in addition to the probabilistic associations among variables found in a belief network.


 

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