Quotient normalized maximum likelihood criterion for learning Bayesian network structures

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Conference article in proceedings
Date
2018-01-01
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Language
en
Pages
10
948-957
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International Conference on Artificial Intelligence and Statistics, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands, Proceedings of Machine Learning Research, Volume 84
Abstract
We introduce an information theoretic criterion for Bayesian network structure learning which we call quotient normalized maximum likelihood (qNML). In contrast to the closely related factorized normalized maximum likelihood criterion, qNML satisfies the property of score equivalence. It is also decomposable and completely free of adjustable hyperparameters. For practical computations, we identify a remarkably accurate approximation proposed earlier by Szpankowski and Weinberger. Experiments on both simulated and real data demonstrate that the new criterion leads to parsimonious models with good predictive accuracy.
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Silander , T , Leppä-Aho , J , Jääsaari , E & Roos , T 2018 , Quotient normalized maximum likelihood criterion for learning Bayesian network structures . in A Storkey & F Perez-Cruz (eds) , International Conference on Artificial Intelligence and Statistics, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands . Proceedings of Machine Learning Research , vol. 84 , JMLR , pp. 948-957 , International Conference on Artificial Intelligence and Statistics , Playa Blanca , Spain , 09/04/2018 . < http://proceedings.mlr.press/v84/silander18a.html >