Comparing Poisson and Gaussian channels (extended)

Abstract

Consider a pair of input distributions which after passing through a Poisson channel become $\epsilon$-close in total variation. We show that they must necessarily then be $\epsilon^{0.5+o(1)}$-close after passing through a Gaussian channel as well. In the opposite direction, we show that distributions inducing $\epsilon$-close outputs over the Gaussian channel must induce $\epsilon^{1+o(1)}$-close outputs over the Poisson. This quantifies a well-known intuition that ''smoothing'' induced by Poissonization and Gaussian convolution are similar. As an application, we improve a recent upper bound of Han-Miao-Shen'2021 for estimating mixing distribution of a Poisson mixture in Gaussian optimal transport distance from $n^{-0.1 + o(1)}$ to $n^{-0.25 + o(1)}$.Comment: 7 pages, 2 figures, to appear in the 2023 IEEE International Symposium on Information Theory (ISIT