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The Bayesian information criterion: background, derivation, and applications
Author(s) -
Neath Andrew A.,
Cavanaugh Joseph E.
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.199
Subject(s) - bayesian information criterion , bayesian probability , computer science , model selection , graphical model , statistical model , bayesian statistics , information criteria , bayes factor , posterior probability , bayesian inference , machine learning , exploratory data analysis , artificial intelligence , data mining
The Bayesian information criterion (BIC) is one of the most widely known and pervasively used tools in statistical model selection. Its popularity is derived from its computational simplicity and effective performance in many modeling frameworks, including Bayesian applications where prior distributions may be elusive. The criterion was derived by Schwarz ( Ann Stat 1978, 6:461–464) to serve as an asymptotic approximation to a transformation of the Bayesian posterior probability of a candidate model. This article reviews the conceptual and theoretical foundations for BIC, and also discusses its properties and applications. WIREs Comput Stat 2012, 4:199–203. doi: 10.1002/wics.199 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Statistical and Graphical Methods of Data Analysis > Information Theoretic Methods Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods