Bayesian Information Criterion (BIC)

When a model involving q parameters is fitted to data with n observations, the Bayesian Information criterion is defined as  - 2 Lq + q * ln(n), where Lq is the maximized log-likelihood.  This goodness of fit statistic adjusts for the number of parameters estimated as well as for the given amount of data. This is closely related to AIC.  In STATISTICA, the BIC can be used in the Generalized Linear/Nonlinear Models (GLZ) module to evaluate the fit of a model.