PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Factorized normalized maximum likelihood criterion for learning Bayesian network structures
Tomi Silander, Teemu Roos, Petri Kontkanen and Petri Myllymäki
In: 4th European Workshop on Probabilistic Graphical Models, 17-19 Sep 2008, Hirtshals, Denmark.


This paper introduces a new scoring criterion, factorized normalized maximum likelihood, for learning Bayesian network structures. The proposed scoring criterion requires no parameter tuning, and it is decomposable and asymptotically consistent. We compare the new scoring criterion to other scoring criteria and describe its practical implementation. Empirical tests confirm its good performance.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:4189
Deposited By:Teemu Roos
Deposited On:23 October 2008