Title

Comparison of Lauritzen-Spiegelhalter and successive restrictions algorithms for computing probability distributions in Bayesian networks

Source of Publication

AIP Conference Proceedings

Abstract

© 2016 Author(s). The basic task of any probabilistic inference system in Bayesian networks is computing the posterior probability distribution for a subset or subsets of random variables, given values or evidence for some other variables from the same Bayesian network. Many methods and algorithms have been developed to exact and approximate inference in Bayesian networks. This work compares two exact inference methods in Bayesian networks-Lauritzen-Spiegelhalter and the successive restrictions algorithm-from the perspective of computational efficiency. The two methods were applied for comparison to a Chest Clinic Bayesian Network. Results indicate that the successive restrictions algorithm shows more computational efficiency than the Lauritzen-Spiegelhalter algorithm.

Document Type

Conference Proceeding

ISBN

['9780735413962']

Publication Date

6-2-2016

DOI

10.1063/1.4952522

Author First name, Last name, Institution

Linda Smail, Zayed University

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