The Akaike Information Criterion (AIC) is a statistical measure used to evaluate the quality of a model by balancing goodness of fit and model complexity. AIC helps in model selection by penalizing models that are overly complex, thus preventing overfitting while allowing for an accurate representation of the data. Lower AIC values indicate a better fit, making it a valuable tool in maximum likelihood estimation for determining the most appropriate model among a set of candidates.
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