is about is_specified_input_of measurement datum statistical model Bayesian information criterion Alejandra Gonzalez-Beltran BIC Bayesian information criterion or Schwartz's Bayesian information criterion 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 the Akaike information criterion (AIC). Given any two estimated models, the model with the lower value of BIC is the one to be preferred. The BIC is an increasing function of sigma_e^2 and an increasing function of k. That is, unexplained variation in the dependent variable and the number of explanatory variables increase the value of BIC. Hence, lower BIC implies either fewer explanatory variables, better fit, or both. Orlaith Burke Philippe Rocca-Serra SBIC Schwartz's Baeysian information criterion Schwarz, Gideon E. (1978). "Estimating the dimension of a model". Annals of Statistics 6 (2): 461–464. doi:10.1214/aos/1176344136. http://en.wikipedia.org/wiki/Bayesian_information_criterion http://www.ncbi.nlm.nih.gov/pubmed/7791040 statistical model selection metadata incomplete