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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

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

We show that $h_\infty(X+Y)\leq h_\infty(Z+W)$, where $X, Y$ are independent log-concave random variables, and $Z, W$ are exponential random variables having the same respective $\infty$-R\'enyi entropies. Analogs for integer-valued…

Probability · Mathematics 2025-11-03 Zhen Fu , Jiange Li

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

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

We prove a new extremal inequality, motivated by the vector Gaussian broadcast channel and the distributed source coding with a single quadratic distortion constraint problems. As a corollary, this inequality yields a generalization of the…

Information Theory · Computer Science 2007-07-13 Tie Liu , Pramod Viswanath

The conditional entropy power inequality is a fundamental inequality in information theory, stating that the conditional entropy of the sum of two conditionally independent vector-valued random variables each with an assigned conditional…

Quantum Physics · Physics 2019-02-01 Giacomo De Palma

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

The sumset and inverse sumset theories of Freiman, Pl\"{u}nnecke and Ruzsa, give bounds connecting the cardinality of the sumset $A+B=\{a+b\;;\;a\in A,\,b\in B\}$ of two discrete sets $A,B$, to the cardinalities (or the finer structure) of…

Information Theory · Computer Science 2015-05-07 Ioannis Kontoyiannis , Mokshay Madiman

We tighten the Entropy Power Inequality (EPI) when one of the random summands is Gaussian. Our strengthening is closely connected to the concept of strong data processing for Gaussian channels and generalizes the (vector extension of)…

Information Theory · Computer Science 2016-02-10 Thomas A. Courtade

The paper establishes the equality condition in the I-MMSE proof of the entropy power inequality (EPI). This is done by establishing an exact expression for the deficit between the two sides of the EPI. Interestingly, a necessary condition…

Information Theory · Computer Science 2017-03-23 Alex Dytso , Ronit Bustin , H. Vincent Poor , Shlomo Shamai

Two new information-theoretic methods are introduced for establishing Poisson approximation inequalities. First, using only elementary information-theoretic techniques it is shown that, when $S_n=\sum_{i=1}^nX_i$ is the sum of the (possibly…

Probability · Mathematics 2010-10-21 Ioannis Kontoyiannis , Peter Harremoes , Oliver Johnson

In this letter, we give a concise, closed-form expression for the differential entropy of the sum of two independent, non-identically-distributed exponential random variables. The derivation is straightforward, but such a concise entropy…

Information Theory · Computer Science 2016-09-12 Andrew W. Eckford , Peter J. Thomas

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

Recent advances have linked various statements involving sumsets and cardinalities with corresponding statements involving sums of random variables and entropies. In this vein, this paper shows that the quantity $2{\bf H}\{X, Y\} - {\bf…

Combinatorics · Mathematics 2026-05-27 Marcel K. Goh

We establish a lower bound on the entropy of weighted sums of (possibly dependent) random variables $(X_1, X_2, \dots, X_n)$ possessing a symmetric joint distribution. Our lower bound is in terms of the joint entropy of $(X_1, X_2, \dots,…

Information Theory · Computer Science 2018-01-16 Jing Hao , Varun Jog

A new sharp inequality featuring the differential R\'enyi entropy, the R\'enyi divergence and the R\'enyi cross-entropy of a pair of probability density functions is established. The equality is reached when one of the probability density…

Information Theory · Computer Science 2026-03-10 Razvan Gabriel Iagar , David Puertas-Centeno

Motivated by the entropy computations relevant to the evaluation of decrease in entropy in bit reset operations, the authors investigate the deficit in an entropic inequality involving two independent random variables, one continuous and…

Information Theory · Computer Science 2018-09-21 James Melbourne , Saurav Talukdar , Shreyas Bhaban , Murti V. Salapaka

This paper proposes a unifying variational approach for proving and extending some fundamental information theoretic inequalities. Fundamental information theory results such as maximization of differential entropy, minimization of Fisher…

Information Theory · Computer Science 2016-02-05 Sangwoo Park , Erchin Serpedin , Khalid Qaraqe

Using a sharp version of the reverse Young inequality, and a R\'enyi entropy comparison result due to Fradelizi, Madiman, and Wang, the authors are able to derive R\'enyi entropy power inequalities for log-concave random vectors when…

Information Theory · Computer Science 2018-07-24 Arnaud Marsiglietti , James Melbourne