In statistics, the Bayesian information criterion (BIC) or Schwarz criterion (also SBC, SBIC) is a criterion for model selection among a finite set of models. It is based, in part, on the likelihood function, and it is closely related to Akaike information criterion (AIC).
When fitting models, it is possible to increase the likelihood by adding parameters, but doing so may result in overfitting. The BIC resolves this problem by introducing a penalty term for the number of parameters in the model. The penalty term is larger in BIC than in AIC.
The BIC was developed by Gideon E. Schwarz, who gave a Bayesian argument for adopting it. It is closely related to the Akaike information criterion (AIC). In fact, Akaike was so impressed with Schwarz's Bayesian formalism that he developed his own Bayesian formalism, now often referred to as the ABIC for "a Bayesian Information Criterion" or more casually "Akaike's Bayesian Information Criterion".
Read more about Bayesian Information Criterion: Mathematically, Characteristics of The Bayesian Information Criterion, Applications
Famous quotes containing the words information and/or criterion:
“Theories of child development and guidelines for parents are not cast in stone. They are constantly changing and adapting to new information and new pressures. There is no “right” way, just as there are no magic incantations that will always painlessly resolve a child’s problems.”
—Lawrence Kutner (20th century)
“There is only one art, whose sole criterion is the power, the authenticity, the revelatory insight, the courage and suggestiveness with which it seeks its truth.... Thus, from the standpoint of the work and its worth it is irrelevant to which political ideas the artist as a citizen claims allegiance, which ideas he would like to serve with his work or whether he holds any such ideas at all.”
—Václav Havel (b. 1936)