The information criteria measure the goodness of fit of an estimated statistical model. The
information criteria (IC) are tests between models - a tool for model selection. Given a data
set, several competing models may be ranked according to their IC, with the one having the lowest
IC being the best. From the IC value one may infer that e.g., the top three models are in a tie
and the rest are far worse, but it would be arbitrary to assign a value above which a given model
Akaike's information criterion is a measure of the goodness of fit of an estimated statistical
model. It is grounded in the concept of entropy, in effect offering a relative measure of the
information lost when a given model is used to describe reality and can be said to describe the
tradeoff between bias and variance in model construction, or loosely speaking that of accuracy
and complexity of the model.
The BIC is very closely related to the Akaike information criterion (AIC). In BIC, the penalty
for additional parameters is stronger than that of the AIC.