BIC is an acronym for Bayesian Information Criterion. BIC is one method of model comparison. See also AIC
Given a finite set of models, BIC (Bayesian Information Criterion) is used to select a model, where lower BIC is generally preferred. It is closely related to another model selection criterion: aic.
BIC is formally defined as
$$\text{BIC} = k\ln(n)-2\ln({\hat{L}})$$
where $k$ is the number of parameters in the statistical model, $n$ the sample size, and $\hat{L}$ the maximized value of the likelihood function of the model.
BIC was introduced in Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 461-464.
