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Learning Bayesian networks from data: An information-theory based approach

✍ Scribed by Jie Cheng; Russell Greiner; Jonathan Kelly; David Bell; Weiru Liu

Publisher
Elsevier Science
Year
2002
Tongue
English
Weight
661 KB
Volume
137
Category
Article
ISSN
0004-3702

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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