Bayesian Statistics
The Akaike Information Criterion (AIC) is a statistical measure used to compare and select models based on their goodness of fit while penalizing for model complexity. It provides a way to quantify the trade-off between the accuracy of a model and the number of parameters it uses, thus facilitating model comparison. A lower AIC value indicates a better-fitting model, making it a crucial tool in likelihood-based inference and model selection processes.
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