Bayesian information criterion

Summary

The Bayesian information criterion (BIC), also called the Schwarz criterion, scores a fitted model by its maximized likelihood with a penalty of $\ln n$ per parameter. Among candidate models, one prefers the model with the smallest BIC. It comes from a large-sample approximation of the marginal likelihood of a model in Bayesian inference. Its penalty is heavier than that of the Akaike information criterion once $n \ge 8$.