This paper presents a method for n-gram language model adaptation based on the principle of minimum discrimination information. A background language model is adapted to fit constraints on its marginal distributions that are derived from new observed data. This work gives a different derivation of the model by Kneser et al.(1997) and extends its application to interpolated language models. The proposed method has been evaluated on an Italian 60K-word broadcast news task.