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Penniless propagation in join trees
Author(s) -
Cano Andrés,
Moral Serafín,
Salmerón Antonio
Publication year - 2000
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/1098-111x(200011)15:11<1027::aid-int4>3.0.co;2-#
Subject(s) - join (topology) , computer science , theoretical computer science , artificial intelligence , mathematics , combinatorics
This paper presents non‐random algorithms for approximate computation in Bayesian networks. They are based on the use of probability trees to represent probability potentials, using the Kullback‐Leibler cross entropy as a measure of the error of the approximation. Different alternatives are presented and tested in several experiments with difficult propagation problems. The results show how it is possible to find good approximations in short time compared with Hugin algorithm. © 2000 John Wiley & Sons, Inc.