Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In statistics, the Bayesian information criterion or Schwarz Criterion is a criterion for model selection among a class of parametric models with different numbers of parameters. Choosing a model to optimize BIC is a form of regularization. When estimating model parameters using maximum likelihood estimation, it is possible to increase the likelihood by adding parameters, which may result in overfitting. The BIC resolves this problem by introducing a penalty term for the number of parameters in the model. The BIC was developed by Gideon E. Schwarz, who gave a Bayesian argument for adopting it. It is very closely related to the Akaike information criterion. In BIC, the penalty for additional parameters is stronger than that of the AIC

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