Learning dynamic Bayesian Network models via cross-validation

by J. M. Peña, J. Björkegren, J. Tegner
Year:2005

Bibliography

​Learning dynamic Bayesian Network models via cross-validation
J.M. Peña, J. Björkegrenand, J. Tegnér
Pattern Recognition Letters, 26 (14), 2295-2308, 2005

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.

DOI: 10.1016/j.patrec.2005.04.005

Learning dynamic Bayesian Network models via cross-validation.pdf

Keywords

Cross-validation Dynamic Bayesian network models Learning
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