Learning dynamic Bayesian network models via cross-validation

Authors:
Jose M. Peña;Johan Björkegren;Jesper Tegnér
Affiliations:
Computational Biology, Department of Physics, Linköping University, 58183 Linköping, Sweden;Center for Genomics and Bioinformatics, Karolinska Institute, 17177 Stockholm, Sweden;Computational Biology, Department of Physics, Linköping University, 58183 Linköping, Sweden and Center for Genomics and Bioinformatics, Karolinska Institute, 17177 Stockholm, Sweden
Venue:
Pattern Recognition Letters
Year:
2005

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Abstract

We study cross-validation as a scoring criterion for learning dynamic Bayesian network models that generalize well. We argue that cross-validation is more suitable than the Bayesian scoring criterion for one of the most common interpretations of generalization. We confirm this by carrying out an experimental comparison of cross-validation and the Bayesian scoring criterion, as implemented by the Bayesian Dirichlet metric and the Bayesian information criterion. The results show that cross-validation leads to models that generalize better for a wide range of sample sizes.