Normative selection of Bayesian networks

Authors:
Paola Sebastiani;Marco Ramoni
Affiliations:
Department of Biostatistics, Boston University, 715 Albany St., T-E417, Boston, MA;Children's Hospital Informatics Program, Harvard Medical School
Venue:
Journal of Multivariate Analysis
Year:
2005

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Abstract

This paper presents a Bayesian decision theoretic foundation to the selection of a Bayesian network from data. We introduce the class of disintegrable loss functions to diversify the loss incurred in choosing different models. Disintegrable loss functions can iteratively be built from simple 0-L loss functions over pair-wise model comparisons and decompose the search for the model with minimum risk into a sequence of local searches, thus retaining the modularity of the model selection procedures for Bayesian networks.