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Approximate discrete entropy monotonicity for log-concave sums

Part of: Communication, information Distribution theory - Probability

Published online by Cambridge University Press:  13 November 2023

Lampros Gavalakis Open the ORCID record for Lampros Gavalakis [Opens in a new window]
Lampros Gavalakis*
Affiliation:
Laboratoire d’Analyse et de Mathématiques Appliquées, Université Gustave Eiffel, Champs-sur-Marne, France
*
Email: lampros.gavalakis@univ-eiffel.fr
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Abstract

It is proven that a conjecture of Tao (2010) holds true for log-concave random variables on the integers: For every $n \geq 1$, if $X_1,\ldots,X_n$ are i.i.d. integer-valued, log-concave random variables, then

\begin{equation*} H(X_1+\cdots +X_{n+1}) \geq H(X_1+\cdots +X_{n}) + \frac {1}{2}\log {\Bigl (\frac {n+1}{n}\Bigr )} - o(1) \end{equation*}
as $H(X_1) \to \infty$, where $H(X_1)$ denotes the (discrete) Shannon entropy. The problem is reduced to the continuous setting by showing that if $U_1,\ldots,U_n$ are independent continuous uniforms on $(0,1)$, then
\begin{equation*} h(X_1+\cdots +X_n + U_1+\cdots +U_n) = H(X_1+\cdots +X_n) + o(1), \end{equation*}
as $H(X_1) \to \infty$, where $h$ stands for the differential entropy. Explicit bounds for the $o(1)$-terms are provided.

Keywords

Entropymonotonicitylog-concavityentropy power inequalitycentral limit theoremconcentrationsumset inequalities

MSC classification

Primary: 94A17: Measures of information, entropy
Secondary: 60E15: Inequalities; stochastic orderings 39B62: Functional inequalities, including subadditivity, convexity, etc.

Information

Type
Paper
Information
Combinatorics, Probability and Computing , Volume 33 , Issue 2 , March 2024 , pp. 196 - 209
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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Footnotes

L.G. has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 101034255.

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