Local Pinsker Inequalities via Stein's Discrete Density Approach

Abstract

Pinsker's inequality states that the relative entropy between two random variables X and Y dominates the square of the total variation distance between X and Y. In this paper, we introduce generalized Fisher information distances and prove that these also dominate the square of the total variation distance. To this end, we introduce a general discrete Stein operator for which we prove a useful covariance identity. We illustrate our approach with several examples. Whenever competitor inequalities are available in the literature, the constants in ours are at least as good, and, in several cases, better.