Nonlinear Optimization
BIC, or Bayesian Information Criterion, is a statistical criterion used to evaluate the goodness of fit of a model while penalizing for the number of parameters. It helps in model selection by balancing the complexity of the model against its performance, making it particularly useful in regularization and feature selection. BIC is derived from the likelihood function and incorporates a penalty term that increases with the number of parameters, promoting simpler models that perform well.
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