The Bayesian Information Criterion (BIC) is a statistical tool used for model selection among a finite set of models. It helps in determining the best-fitting model by balancing the goodness of fit with the complexity of the model, penalizing those that are overly complex to avoid overfitting. In the context of nonlinear regression models, BIC assists in comparing different nonlinear models to identify which one explains the data best without being too complicated.
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