A simulated annealing-based method for learning Bayesian networks from statistical data: Research Articles

Authors:
Martin Janžura;Jan Nielsen
Affiliations:
Institute of Information Theory and Automation, Prague, Czech Republic;Institute of Information Theory and Automation, Prague, Czech Republic
Venue:
International Journal of Intelligent Systems - Uncertainty Processing
Year:
2006

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Abstract

The problem of learning Bayesian networks from statistical data is described and reformulated as a discrete optimization problem. For a solution we employ the stochastic algorithm that is known as simulated annealing and that is based on the Markov Chain Monte Carlo approach. Numerical examples are included to illustrate the efficiency of the method. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 335–348, 2006.