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An optimization-based approach for the design of Bayesian networks. (English) Zbl 1187.90086
Summary: Bayesian networks model conditional dependencies among the domain variables, and provide a way to deduce their interrelationships as well as a method for the classification of new instances. One of the most challenging problems in using Bayesian networks, in the absence of a domain expert who can dictate the model, is inducing the structure of the network from a large, multivariate data set. We propose a new methodology for the design of the structure of a Bayesian network based on concepts of graph theory and nonlinear integer optimization techniques.

MSC:
90B15 Stochastic network models in operations research
62H05 Characterization and structure theory for multivariate probability distributions; copulas
62K05 Optimal statistical designs
Software:
UCI-ml
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