A Note on the Generalized Information Criterion for Choice of a Model
本文探讨了在模型选择中，最大化对数似然减去参数个数乘数这一准则中乘数的取值选择，其中乘数为1对应Akaike信息准则，并提及与贝叶斯方法的关系。
One way of selecting models is to choose that model for which the maximized log likelihood minus a multiple of the number of parameters estimated is a maximum. This note explores the choice of values for the multiplier, with the value one corresponding to Akaike's information criterion. The relationship with Bayesian procedures is mentioned.