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In this brief note, we study the strong law of large numbers for random walks in random scenery. Under the assumptions that the random scenery is non-stationary and satisfies weakly dependent condition with an appropriate rate, we establish…

Probability · Mathematics 2025-02-11 Sadillo Sharipov

We consider dynamical systems on a finite measure space fulfilling a spectral gap property and Birkhoff sums of a non-negative, non-integrable observable. For such systems we generalize strong laws of large numbers for intermediately…

Dynamical Systems · Mathematics 2019-09-04 Marc Kesseböhmer , Tanja Schindler

Zipf's law is just one out of many universal laws proposed to describe statistical regularities in language. Here we review and critically discuss how these laws can be statistically interpreted, fitted, and tested (falsified). The modern…

Physics and Society · Physics 2016-05-27 Eduardo G. Altmann , Martin Gerlach

Conditional independence and Markov properties are powerful tools allowing expression of multidimensional probability distributions by means of low-dimensional ones. As multidimensional possibilistic models have been studied for several…

Artificial Intelligence · Computer Science 2013-01-18 Jirina Vejnarova

Under the sublinear expectation $\mathbb{E}[\cdot]:=\sup_{\theta\in \Theta} E_\theta[\cdot]$ for a given set of linear expectations $\{E_\theta: \theta\in \Theta\}$, we establish a new law of large numbers and a new central limit theorem…

Probability · Mathematics 2018-05-16 Xiao Fang , Shige Peng , Qi-Man Shao , Yongsheng Song

In the article we generalize the Marcinkiewicz sampling theorem in the context of Orlicz spaces. We establish conditions under which sampling theorem holds in terms of restricted submultiplicativity and supermultiplicativity of an…

Functional Analysis · Mathematics 2022-09-13 Aleksander Pawlewicz , Michał Wojciechowski

The paper deals with conditional linear information inequalities valid for entropy functions induced by discrete random variables. Specifically, the so-called conditional Ingleton inequalities are in the center of interest: these are valid…

Information Theory · Computer Science 2022-03-16 Milan Studeny

We explore negative dependence and stochastic orderings, showing that if an integer-valued random variable $W$ satisfies a certain negative dependence assumption, then $W$ is smaller (in the convex sense) than a Poisson variable of equal…

Probability · Mathematics 2016-01-22 Fraser Daly

In terms of the Dirac representation of sample mean and the weak convergence of empirical distributions that holds almost surely, we construct a new proof for a strong law of large numbers of Kolmogorov's type with i.i.d. random variables…

Probability · Mathematics 2020-09-02 Yu-Lin Chou

We investigate the spectral distribution of random matrix ensembles with correlated entries. We consider symmetric matrices with real valued entries and stochastically independent diagonals. Along the diagonals the entries may be…

Probability · Mathematics 2015-03-13 Olga Friesen , Matthias Löwe

We present a simple and versatile method for deriving (an)isotropic local laws for general random matrices constructed from independent random variables. Our method is applicable to mean-field random matrices, where all independent…

Probability · Mathematics 2017-04-13 Yukun He , Antti Knowles , Ron Rosenthal

We revisit the problem of condensation for independent, identically distributed random variables with a power-law tail, conditioned by the value of their sum. For large values of the sum, and for a large number of summands, a condensation…

Statistical Mechanics · Physics 2022-03-03 Claude Godrèche

We prove a Bernstein-type bound for the difference between the average of negative log-likelihoods of independent discrete random variables and the Shannon entropy, both defined on a countably infinite alphabet. The result holds for the…

Information Theory · Computer Science 2021-06-24 Yunpeng Zhao

In this paper, we expand and generalize the findings presented in our previous work on the law of large numbers and the large deviation principle for Poisson processes with uniform catastrophes. We study three distinct scalings: sublinear…

Probability · Mathematics 2025-05-29 A. Logachov , O. Logachova , A. Yambartsev

We investigate operator-valued monotone independence, a noncommutative version of independence for conditional expectation. First we introduce operator-valued monotone cumulants to clarify the whole theory and show the moment-cumulant…

Operator Algebras · Mathematics 2014-09-09 Takahiro Hasebe , Hayato Saigo

Power-law distributions are typical macroscopic features occurring in almost all complex systems observable in nature. As a result, researchers in quantitative analyses must often generate random synthetic variates obeying power-law…

Physics and Society · Physics 2014-11-11 Filippo Radicchi

This paper introduces a decision-theoretic framework for constructing and evaluating test statistics based on their relationship with ancillary statistics-quantities whose distributions remain fixed under the null and alternative…

Methodology · Statistics 2026-04-03 Albert Vexler , Douglas Landsittel

We give a short, self-contained, and elementary proof of the strong law of large numbers under a power law decay hypothesis for joint second moments. The result is related to the classical one by Lyons. However, we also provide a rate of…

Probability · Mathematics 2019-11-18 Abdelmalek Abdesselam

In this paper, we introduce the concept of hyperbolic valued random variables, their expectation and moments. We develop the hyperbolic analogue of Binomial and Poisson distributions. We study some of the properties of expectation on the…

Probability · Mathematics 2017-03-28 Romesh Kumar , Kailash Sharma

We present a Law of Large Numbers principle for uniformly continuous random quantum dynamical semigroups. Random iterates of independent copies of these semigroups are shown to be Chernoff equivalent to the quantum dynamical semigroup by…

Mathematical Physics · Physics 2022-03-07 John E. Gough , Yurii N. Orlov , Vsevolod Zh. Sakbaev , Oleg G. Smolyanov