On the Convergence of MDL Density Estimation

This paper presents a general information exponential inequality that measures the statistical complexity of some deterministic and randomized density estimators. Using this inequality, one is able to improve classical results concerning the convergence of two-part code MDL in. Moreover, one is able to derive clean nite-sample convergence bounds that are not obtainable using previous approaches.