AbstractThe 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 Stat1978, 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 Stat2012, 4:199–203. doi: 10.1002/wics.199This article is categorized under:Statistical and Graphical Methods of Data Analysis > Bayesian Methods and TheoryStatistical and Graphical Methods of Data Analysis > Information Theoretic MethodsStatistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods