Comparing Poisson and Gaussian channels (extended)

Anzo Teh; Yury Polyanskiy

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arXiv · 2306.16735

Comparing Poisson and Gaussian channels (extended)

Anzo Teh·Yury Polyanskiy

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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 $n^{-0.25 + o(1)}$.

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Anzo Teh, Yury Polyanskiy. 2023-06-29. Comparing Poisson and Gaussian channels (extended). https://arxiv.org/abs/2306.16735

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