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Various quantum analogues of the central limit theorem, which is one of the cornerstones of probability theory, are known in the literature. One such analogue, due to Cushen and Hudson, is of particular relevance for quantum optics. It…

Quantum Physics · Physics 2021-04-05 Simon Becker , Nilanjana Datta , Ludovico Lami , Cambyse Rouzé

This paper deals with the numerical approximation of normalizing constants produced by particle methods, in the general framework of Feynman-Kac sequences of measures. It is well-known that the corresponding estimates satisfy a central…

Probability · Mathematics 2013-07-02 Jean Bérard , Pierre Del-Moral , Arnaud Doucet

We obtain quenched almost sure invariance principle (with convergence rate) for Random Young Tower. We apply our result to i.i.d perturbations of non-uniformly expanding maps. In particular, we answer one open question in \cite{BBM}.

Dynamical Systems · Mathematics 2020-01-22 Yaofeng Su

For a sequence of i.i.d. random variables $\{\xi_x : x\in \bb Z\}$ bounded above and below by strictly positive finite constants, consider the nearest-neighbor one-dimensional simple exclusion process in which a particle at $x$ (resp.…

Probability · Mathematics 2007-05-23 M. D. Jara , C. Landim

In this paper, we investigate annealed and quenched limit theorems for random expanding dynamical systems. Making use of functional analytic techniques and more probabilistic arguments with martingales, we prove annealed versions of a…

Dynamical Systems · Mathematics 2014-07-18 Romain Aimino , Matthew Nicol , Sandro Vaienti

We establish central limit theorems for a large class of supercritical branching Markov processes in infinite dimension with spatially dependent and non-necessarily local branching mechanisms. This result relies on a fourth moment…

Probability · Mathematics 2025-01-31 Bertrand Cloez , Nicolás Zalduendo

The paper is devoted to the investigation of Esscher's transform on high dimensional Euclidean spaces in the light of its application to the central limit theorem. With this tool, we explore necessary and sufficient conditions of normal…

Probability · Mathematics 2024-07-31 Sergey Bobkov , Friedrich Götze

We give optimal convergence rates in the central limit theorem for a large class of martingale difference sequences with bounded third moments. The rates depend on the behaviour of the conditional variances and for stationary sequences the…

Probability · Mathematics 2007-05-23 Mohamed El Machkouri , Lahcen Ouchti

The limiting distribution of the normalized number of comparisons used by Quicksort to sort an array of n numbers is known to be the unique fixed point with zero mean of a certain distributional transformation S. We study the convergence to…

Probability · Mathematics 2007-05-23 James Allen Fill , Svante Janson

We are interested in a fragmentation process. We observe fragments frozen when their sizes are less than {\epsilon} ({\epsilon} > 0). It is known ([BM05]) that the empirical measure of these fragments converges in law, under some…

Probability · Mathematics 2022-10-17 Camille Noûs , Sylvain Rubenthaler

We consider a ballistic random walk in an i.i.d. random environment that does not allow retreating in a certain fixed direction. We prove an invariance principle (functional central limit theorem) under almost every fixed environment. The…

Probability · Mathematics 2016-08-14 Firas Rassoul-Agha , Timo Seppäläinen

A central limit theorem for arrays of symmetric row-wise exchangeable random variables is presented. The result is valid for finite and infinite extendable and non-extendable sequences. Unlike most reported versions of the central limit…

Probability · Mathematics 2020-06-22 Ilya Soloveychik

We are interested in a fragmentation process. We observe fragments frozen when their sizes are less than $\epsilon$ ($\epsilon$ > 0). Is is known ([BM05]) that the empirical measure of these fragments converges in law, under some…

Probability · Mathematics 2019-07-30 Sylvain Rubenthaler

For noncorrelated random variables, we study a concentration property of the family of distributions of normalized sums formed by sequences of times of a given large length.

Probability · Mathematics 2007-05-23 Sergey G. Bobkov

In this paper, we establish an almost sure central limit theorem for a general random sequence under a strong approximation condition. Additionally, we derive the law of the iterated logarithm for the center of mass corresponding to a…

Probability · Mathematics 2024-07-08 Zhishui Hua , Wei Wanga , Liang Dong

We prove a quenched central limit theorem for balanced random walks in time dependent ergodic random environments which is not necessarily nearest-neigbhor. We assume that the environment satisfies appropriate ergodicity and ellipticity…

Probability · Mathematics 2016-09-06 Jean-Dominique Deuschel , Xiaoqin Guo , Alejandro F. Ramirez

We prove a quenched functional central limit theorem for a one-dimensional random walk driven by a simple symmetric exclusion process. This model can be viewed as a special case of the random walk in a balanced random environment, for which…

Probability · Mathematics 2021-07-20 Otávio Menezes , Jonathon Peterson , Yongjia Xie

We consider generalized $(T, T^{-1})$ transformations such that the base map satisfies a multiple mixing local limit theorem and anticoncentration large deviation bounds and in the fiber we have $\mathbb{R}^d$ actions with $d=1$ or $2$…

Dynamical Systems · Mathematics 2023-05-09 Dmitry Dolgopyat , Changguang Dong , Adam Kanigowski , Peter Nandori

Linear processes are defined as a discrete-time convolution between a kernel and an infinite sequence of i.i.d. random variables. We modify this convolution by introducing decimation, that is, by stretching time accordingly. We then…

Statistics Theory · Mathematics 2008-12-18 François Roueff , Murad S. Taqqu

We study central limit theorems for certain nonlinear sequences of random variables. In particular, we prove the central limit theorems for the bounded conductivity of the random resistor networks on hierarchical lattices.

Disordered Systems and Neural Networks · Physics 2007-05-23 Jung M. Woo , Jan Wehr