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In this chapter, we discuss using Kullback–Leibler information as a criterion for evaluating statistical models that approximate the true probability distribution of the data and its properties. We also explain how this criterion for evaluating statistical models leads to the concept of the information criterion, AIC. To this end, we explain the basic framework of model evaluation and the derivation of AIC by adopting a unified approach.

Keywords

Akaike Information Criterion Maximum Likelihood Estimator Variance Covariance Matrix True Order True Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2008

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