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

✍ Scribed by Martin Janžura; Jan Nielsen

Publisher: John Wiley and Sons
Year: 2006
Tongue: English
Weight: 145 KB
Volume: 21
Category: Article
ISSN: 0884-8173
DOI: 10.1002/int.20138

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✦ Synopsis

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.

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