In this paper we adapt the AIC model selection rule to be used in combination with prior estimated noise models in the presence of model errors. It is shown that under these conditions a slightly modified cost function extension is needed, resulting in a multiplicative complexity term instead of an additive one as would be found for fixed noise models and no model errors. The equivalency of this result with the classical prediction error framework is discussed.