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This paper considers the problem of sequential empirical coordination, where the objective is to achieve a given value of the expected uniform deviation between state-action empirical averages and statistical expectations under a given…

Information Theory · Computer Science 2018-06-12 Ehsan Shafieepoorfard , Maxim Raginsky

We investigate the asymptotic normality of the posterior distribution in the discrete setting, when model dimension increases with sample size. We consider a probability mass function $\theta_0$ on $\mathbbm{N}\setminus \{0\}$ and a…

Statistics Theory · Mathematics 2009-01-29 S. Boucheron , E. Gassiat

The Shannon entropy is a fundamental measure for quantifying diversity and model complexity in fields such as information theory, ecology, and genetics. However, many existing studies assume that the number of species is known, an…

Methodology · Statistics 2026-02-23 Takato Hashino , Koji Tsukuda

Discovery problems often require deciding whether additional sampling is needed to detect all categories whose prevalence exceeds a prespecified threshold. We study this question under a Bernoulli product (incidence) model, where categories…

Methodology · Statistics 2026-01-29 Alessandro Colombi , Mario Beraha , Amichai Painsky , Stefano Favaro

We show that the essential properties of entropy (monotonicity, additivity and subadditivity) are consequences of entropy being a monoidal natural transformation from the under category functor $-/\mathsf{LProb}_{\rho}$ (where…

Category Theory · Mathematics 2024-04-16 Cheuk Ting Li

In this paper, we are concerned with obtaining distribution-free concentration inequalities for mixture of independent Bernoulli variables that incorporate a notion of variance. Missing mass is the total probability mass associated to the…

Machine Learning · Statistics 2015-03-05 Bahman Yari Saeed Khanloo

From the output produced by a memoryless deletion channel from a uniformly random input of known length $n$, one obtains a posterior distribution on the channel input. The difference between the Shannon entropy of this distribution and that…

Information Theory · Computer Science 2018-08-01 Arash Atashpendar , David Mestel , A. W. Roscoe , Peter Y. A. Ryan

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

Many axiomatic definitions of entropy, such as the R\'enyi entropy, of a random variable are closely related to the $\ell_{\alpha}$-norm of its probability distribution. This study considers probability distributions on finite sets, and…

Information Theory · Computer Science 2016-05-06 Yuta Sakai , Ken-ichi Iwata

How low can the joint entropy of $n$ $d$-wise independent (for $d\ge2$) discrete random variables be, subject to given constraints on the individual distributions (say, no value may be taken by a variable with probability greater than $p$,…

Discrete Mathematics · Computer Science 2022-04-05 Dmytro Gavinsky , Pavel Pudlák

We show that the set of atoms of the limiting empirical marginal distribution in the random $2$-SAT model is $\mathbb Q \cap (0,1)$, for all clause-to-variable densities up to the satisfiability threshold. While for densities up to $1/2$,…

Probability · Mathematics 2024-10-24 Noela Müller , Ralph Neininger , Haodong Zhu

Bercovici and Pata showed that the correspondence between classically, freely, and Boolean infinitely divisible distributions holds on the level of limit theorems. We extend this correspondence also to distributions infinitely divisible…

Operator Algebras · Mathematics 2013-02-20 Michael Anshelevich , John D. Williams

Hypothesis testing is a fundamental issue in statistical inference and has been a crucial element in the development of information sciences. The Chernoff bound gives the minimal Bayesian error probability when discriminating two hypotheses…

Quantum Physics · Physics 2009-11-13 J. Calsamiglia , R. Munoz-Tapia , Ll. Masanes , A. Acin , E. Bagan

We derive simple but nearly tight upper and lower bounds for the binomial lower tail probability (with straightforward generalization to the upper tail probability) that apply to the whole parameter regime. These bounds are easy to compute…

Probability · Mathematics 2022-11-04 Huangjun Zhu , Zihao Li , Masahito Hayashi

How hard is it guess a password? Massey showed that that the Shannon entropy of the distribution from which the password is selected is a lower bound on the expected number of guesses, but one which is not tight in general. In a series of…

Information Theory · Computer Science 2013-02-12 Mark M. Christiansen , Ken R. Duffy

The Shannon entropy of a random variable has much behaviour analogous to a signed measure. Previous work has explored this connection by defining a signed measure on abstract sets, which are taken to represent the information that different…

Information Theory · Computer Science 2025-05-28 Keenan J. A. Down , Pedro A. M. Mediano

Sufficient conditions are developed, under which the compound Poisson distribution has maximal entropy within a natural class of probability measures on the nonnegative integers. Recently, one of the authors [O. Johnson, {\em Stoch. Proc.…

Combinatorics · Mathematics 2013-03-20 Oliver Johnson , Ioannis Kontoyiannis , Mokshay Madiman

Let $\Gamma$ be a countable discrete group, $H$ a lcsc totally disconnected group and $\rho : \Gamma \rightarrow H$ a homomorphism with dense image. We develop a general and explicit technique which provides, for every compact open subgroup…

Dynamical Systems · Mathematics 2020-06-30 Michael Björklund , Yair Hartman , Hanna Oppelmayer

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

A Chernoff-type distribution is a nonnormal distribution defined by the slope at zero of the greatest convex minorant of a two-sided Brownian motion with a polynomial drift. While a Chernoff-type distribution is known to appear as the…

Statistics Theory · Mathematics 2021-06-23 Qiyang Han , Kengo Kato