Naturally, the true distribution is not known to the statistician who is evaluating the model. If it were, then the whole statistical analysis would be useless. However, it turns out that we can give an unbiased estimation of the divergence between the true distribution and the distribution estimated from a particular model,   \[ \text{AIC}[M] = - 2 \log P( s \mid h_{\hat{\theta}(s)} ) + 2 d , \]  in which \(s\) is the sample data, \(\hat{\theta}(s)\) is the maximum likelihood estimate (MLE) of the model \(M\), and \(d = dim(\Theta)\) is the number of dimensions of the parameter space of the model. The MLE of the model thereby features in an expression of the model quality, i.e., in a role that is conceptually distinct from the estimator function.