Bayesian network prediction: algorithm and software implementation

Authors

  • E.D. Maslennikov
  • V.B. Sulimov

Keywords:

Bayesian network
belief network
belief update
expert system
join tree
junction tree
probabilistic interference
probabilistic propagation

Abstract

This paper is devoted to the clustering belief updating algorithm using the junction tree as a tree graph representation of Bayesian networks. The algorithm is applicable for predictions based on a learned Bayesian network as well as for supporting an exact network learning process, for example, the EM algorithm. The constructing steps and the principles of work with the junction tree are specified. The software implementation of the algorithm is also considered.

New computational technologies

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Published

2010-11-22

Issue

Vol. 11 (2010): Issue 4.

Section

Section 2. Programming

Author Biographies

E.D. Maslennikov

Dimonta, LLC
• Student

V.B. Sulimov

Lomonosov Moscow State University,
Research Computing Center
• Head of Laboratory

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