The Bayesian Information Criterion (BIC) is a statistical tool used for model selection among a finite set of models; it provides a criterion for evaluating the goodness of fit of a model while also taking into account the complexity of the model. BIC is particularly useful because it penalizes models that have a large number of parameters, helping to prevent overfitting. It is derived from the likelihood function and includes a penalty term based on the number of parameters and the sample size.
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