The Bayesian Information Criterion (BIC) is a statistical measure used for model selection among a finite set of models, which balances model fit and complexity. It helps to determine the best model by penalizing models that have more parameters, thus preventing overfitting while ensuring that the chosen model has a good fit to the data. The BIC is particularly useful when comparing different types of regression models, including multinomial and ordinal logistic regression, and is essential in evaluating forecasting models and their predictive power in real-world applications.
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