Penniless propagation in join trees

  1. Andrés Cano1,*,
  2. Serafín Moral1,
  3. Antonio Salmerón2

Article first published online: 26 OCT 2000

DOI: 10.1002/1098-111X(200011)15:11<1027::AID-INT4>3.0.CO;2-#

Issue

International Journal of Intelligent Systems

International Journal of Intelligent Systems

Volume 15, Issue 11, pages 1027–1059, November 2000

Additional Information

How to Cite

Cano, A., Moral, S. and Salmerón, A. (2000), Penniless propagation in join trees. Int. J. Intell. Syst., 15: 1027–1059. doi: 10.1002/1098-111X(200011)15:11<1027::AID-INT4>3.0.CO;2-#

Author Information

  1. 1

    Department Computer Science and Artificial Intelligence, University of Granada, Spain

  2. 2

    Department Statistics and Applied Mathematics, University of Almería, Spain

*Department Computer Science and Artificial Intelligence, University of Granada, Spain

Publication History

  1. Issue published online: 26 OCT 2000
  2. Article first published online: 26 OCT 2000

Funded by

  • CICYT. Grant Numbers: TIC97–1135–C04–01, T97–1135–C04–02

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Abstract

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.

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