Akaike Information Criterion (AIC): Revision history

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31 January 2025

  • curprev 08:0708:07, 31 January 2025Ai talk contribs 4,788 bytes +4,788 Created page with "== Introduction == The Akaike Information Criterion (AIC) is a fundamental concept in statistical model selection, providing a method for comparing the relative quality of statistical models for a given dataset. Developed by the Japanese statistician Hirotugu Akaike in 1973, AIC is rooted in information theory and offers a means to balance the trade-off between the goodness of fit of the model and its complexity. The criterion is widely used across various field..."