Theoretical Statistics
BIC, or Bayesian Information Criterion, is a statistical criterion used for model selection among a finite set of models. It helps to identify the best-fitting model while penalizing for the complexity of the model, aiming to prevent overfitting. By balancing goodness of fit with model complexity, BIC provides a way to compare models based on their likelihoods and the number of parameters involved.
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