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Many partially-successful attempts have been made to find the most natural discrete-variable version of Shannon's entropy power inequality (EPI). We develop an axiomatic framework from which we deduce the natural form of a discrete-variable…

Information Theory · Computer Science 2016-11-17 Saikat Guha , Jeffrey H. Shapiro , Raul Garcia-Patron Sanchez

We show that there is equality in Shannon's Entropy Power Inequality (EPI) if and only if the random variables involved are Gaussian, assuming nothing beyond the existence of differential entropies. This is done by justifying de Bruijn's…

Probability · Mathematics 2025-09-30 Lampros Gavalakis , Ioannis Kontoyiannis

It is known that the Entropy Power Inequality (EPI) always holds if the random variables have density. Not much work has been done to identify discrete distributions for which the inequality holds with the differential entropy replaced by…

Information Theory · Computer Science 2012-05-22 Naresh Sharma , Smarajit Das , Siddharth Muthukrishnan

Let $\mathsf{N}_{\rm d}\left[X\right]=\frac{1}{2\pi {\rm e}}{\rm e}^{2\mathsf{H}\left[X\right]}$ denote the entropy power of the discrete random variable $X$ where $\mathsf{H}\left[X\right]$ denotes the discrete entropy of $X$. In this…

Information Theory · Computer Science 2019-05-09 Ehsan Nekouei , Mikael Skoglund , Karl Henrik Johansson

We study the $p$-R\'{e}nyi entropy power inequality with a weight factor $t$ on two independent continuous random variables $X$ and $Y$. The extension essentially relies on a modulation on the sharp Young's inequality due to Bobkov and…

Quantum Physics · Physics 2023-11-28 Junseo Lee , Hyeonjun Yeo , Kabgyun Jeong

We investigate the R\'enyi entropy of independent sums of integer valued random variables through Fourier theoretic means, and give sharp comparisons between the variance and the R\'enyi entropy, for Poisson-Bernoulli variables. As…

Probability · Mathematics 2024-03-19 Mokshay Madiman , James Melbourne , Cyril Roberto

Renyi's "thinning" operation on a discrete random variable is a natural discrete analog of the scaling operation for continuous random variables. The properties of thinning are investigated in an information-theoretic context, especially in…

Information Theory · Computer Science 2010-08-17 Peter Harremoes , Oliver Johnson , Ioannis Kontoyiannis

Shannon's Entropy Power Inequality can be viewed as characterizing the minimum differential entropy achievable by the sum of two independent random variables with fixed differential entropies. The entropy power inequality has played a key…

Information Theory · Computer Science 2012-07-31 Varun Jog , Venkat Anantharam

The entropy power inequality for independent random vectors is a foundational result of information theory, with deep connections to probability and geometric functional analysis. Several extensions of the entropy power inequality have been…

Information Theory · Computer Science 2025-12-23 Mokshay Madiman , James Melbourne , Cyril Roberto

This paper addresses the correspondence between linear inequalities of Shannon entropy and differential entropy for sums of independent group-valued random variables. We show that any balanced (with the sum of coefficients being zero)…

Information Theory · Computer Science 2016-09-12 Ashok Vardhan Makkuva , Yihong Wu

New families of Fisher information and entropy power inequalities for sums of independent random variables are presented. These inequalities relate the information in the sum of $n$ independent random variables to the information contained…

Information Theory · Computer Science 2024-05-07 Mokshay Madiman , Andrew Barron

Yet another simple proof of the entropy power inequality is given, which avoids both the integration over a path of Gaussian perturbation and the use of Young's inequality with sharp constant or R\'enyi entropies. The proof is based on a…

Information Theory · Computer Science 2017-02-22 Olivier Rioul

An "entropy increasing to the maximum" result analogous to the entropic central limit theorem (Barron 1986; Artstein et al. 2004) is obtained in the discrete setting. This involves the thinning operation and a Poisson limit. Monotonic…

Information Theory · Computer Science 2009-11-18 Yaming Yu

We provide a condition under which a version of Shannon's Entropy Power Inequality will hold for dependent variables. We provide information inequalities extending those found in the independent case.

Probability · Mathematics 2007-05-23 Oliver Johnson

A lower bound on the R\'enyi differential entropy of a sum of independent random vectors is demonstrated in terms of rearrangements. For the special case of Boltzmann-Shannon entropy, this lower bound is better than that given by the…

Information Theory · Computer Science 2015-05-07 Liyao Wang , Mokshay Madiman

Shannon entropy for discrete distributions is a fundamental and widely used concept, but its continuous analogue, known as differential entropy, lacks essential properties such as positivity and compatibility with the discrete case. In this…

Probability · Mathematics 2025-08-07 Yuliya Mishura , Kostiantyn Ralchenko

This paper provides tight bounds on the R\'enyi entropy of a function of a discrete random variable with a finite number of possible values, where the considered function is not one-to-one. To that end, a tight lower bound on the R\'enyi…

Information Theory · Computer Science 2018-12-11 Igal Sason

We describe five types of results concerning information and concentration of discrete random variables, and relationships between them, motivated by their counterparts in the continuous case. The results we consider are information…

Probability · Mathematics 2017-04-25 Oliver Johnson

We establish a discrete analog of the R\'enyi entropy comparison due to Bobkov and Madiman. For log-concave variables on the integers, the min entropy is within log e of the usual Shannon entropy. Additionally we investigate the entropic…

Probability · Mathematics 2021-06-01 James Melbourne , Tomasz Tkocz

This paper gives improved R\'{e}nyi entropy power inequalities (R-EPIs). Consider a sum $S_n = \sum_{k=1}^n X_k$ of $n$ independent continuous random vectors taking values on $\mathbb{R}^d$, and let $\alpha \in [1, \infty]$. An R-EPI…

Information Theory · Computer Science 2016-07-21 Eshed Ram , Igal Sason
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