The Bayesian Information Criterion (BIC) is a statistical measure 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 model's complexity and its ability to explain the data, making it particularly useful in contexts where models may vary in their complexity, such as polynomial and non-linear regression. BIC is derived from Bayesian principles, providing a way to compare different models and choose the one that best captures the underlying data structure with an optimal number of parameters.
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