Learning dynamic Bayesian network models via cross-validation

Jose M. Peña*, Johan Björkegren, Jesper Tegner

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)2295-2308
Number of pages14
JournalPattern Recognition Letters
Volume26
Issue number14
DOIs
StatePublished - Oct 15 2005

Keywords

  • Cross-validation
  • Dynamic Bayesian network models
  • Learning.

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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