On Model Selection, Bayesian Networks, and the Fisher Information Integral

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http://hdl.handle.net/10138/158484

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Zou , Y & Roos , T T 2015 , On Model Selection, Bayesian Networks, and the Fisher Information Integral . in J Suzuki & M Ueno (eds) , Advanced Methodologies for Bayesian Networks : Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015, Proceedings . Lecture notes in computer science , vol. 9505 , Lecture notes in artificial intelligence , Springer International Publishing AG , Cham , pp. 122-135 , Workshop on Advanced Methodologies for Bayesian Networks , Yokohama , Japan , 16/11/2015 . https://doi.org/10.1007/978-3-319-28379-1

Title: On Model Selection, Bayesian Networks, and the Fisher Information Integral
Author: Zou, Yuan; Roos, Teemu Teppo
Other contributor: Suzuki, Joe
Ueno, Maomi
Contributor organization: Helsinki Institute for Information Technology
Department of Computer Science
The Finnish Center of Excellence in Computational Inference Research (COIN)
Information, Complexity and Learning research group / Teemu Roos
Complex Systems Computation Group
Publisher: Springer International Publishing AG
Date: 2015
Language: eng
Number of pages: 14
Belongs to series: Advanced Methodologies for Bayesian Networks
Belongs to series: Lecture notes in computer science - Lecture notes in artificial intelligence
ISBN: 978-3-319-28378-4
978-3-319-28379-1
ISSN: 1611-3349
DOI: https://doi.org/10.1007/978-3-319-28379-1
URI: http://hdl.handle.net/10138/158484
Abstract: Abstract. We study BIC-like model selection criteria and in particular, their refinements that include a constant term involving the Fisher information matrix. We observe that for complex Bayesian network models, the constant term is a negative number with a very large absolute value that dominates the other terms for small and moderate sample sizes. We show that including the constant term degrades model selection accuracy dramatically compared to the standard BIC criterion where the term is omitted. On the other hand, we demonstrate that exact formulas such as Bayes factors or the normalized maximum likelihood (NML), or their approximations that are not based on Taylor expansions, perform well. A conclusion is that in lack of an exact formula, one should use either BIC, which is a very rough approximation, or a very close approximation but not an approximation that is truncated after the constant term.
Subject: 113 Computer and information sciences
BIC
NML
BAYESIAN NETWORKS
Fisher information integral
112 Statistics and probability
Peer reviewed: Yes
Usage restriction: openAccess


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