An optimization-based approach for the design of Bayesian networks

Authors:
Ana M. MartíNez-RodríGuez;Jerrold H. May;Luis G. Vargas
Affiliations:
University of Jaen, Spain;The Joseph M Katz Graduate School of Business, University of Pittsburgh, United States;The Joseph M Katz Graduate School of Business, University of Pittsburgh, United States
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2008

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Abstract

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.