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A new approach to the modeling of nonfree particle diffusion is presented. The approach uses a general setup based on geometric graphs (networks of curves), which means that particle diffusion in anything from arrays of barriers and pore…

Statistical Mechanics · Physics 2018-04-05 Niels Buhl

We characterize the convergence in distribution to a standard normal law for a sequence of multiple stochastic integrals of a fixed order with variance converging to 1. Some applications are given, in particular to study the limiting…

Probability · Mathematics 2007-05-23 David Nualart , Giovanni Peccati

In this paper, we investigate a central limit theorem for weighted sums of independent random variables under sublinear expectations. It is turned out that our results are natural extensions of the results obtained by Peng and Li and Shi.

Probability · Mathematics 2011-05-05 Defei Zhang

The central limit theorem ensures that a sum of random variables tends to a Gaussian distribution as their total number tends to infinity. However, for a class of positive random variables, we find that the sum tends faster to a log-normal…

Fluid Dynamics · Physics 2013-10-16 H. Mouri

In probability theory and statistics, the IID model represents a single population, and a large, potentially infinite sample from this population. Main theorems, in particular the central limit theorem and laws of large number (LLN) assure…

Statistics Theory · Mathematics 2017-10-02 Uwe Saint-Mont

Random matrix theory (RMT) provides a successful model for quantum systems, whose classical counterpart has a chaotic dynamics. It is based on two assumptions: (1) matrix-element independence, and (2) base invariance. Last decade witnessed…

Chaotic Dynamics · Physics 2011-09-27 A. Y. Abul-Magd

We study sums of a random multiplicative function; this is an example, of number-theoretic interest, of sums of products of independent random variables (chaoses). Using martingale methods, we establish a normal approximation for the sum…

Number Theory · Mathematics 2010-12-02 Adam J. Harper

We consider a class of random billiards in a tube, where reflection angles at collisions with the boundary of the tube are random variables rather than deterministic (and elastic) quantities. We obtain a (non-standard) Central Limit Theorem…

Dynamical Systems · Mathematics 2025-07-21 Henk Bruin , Niels Kolenbrander , Dalia Terhesiu

In this paper, we study various new Hawkes processes, namely, so-called general compound and regime-switching general compound Hawkes processes to model the price processes in the limit order books. We prove Law of Large Numbers (LLN) and…

Mathematical Finance · Quantitative Finance 2017-06-29 Anatoliy Swishchuk

In this article, we present a brief narration of the origin and the overview of the recent developments done on the Kolkata Paise Restaurant (KPR) problem, which can serve as a prototype for a broader class of resource allocation problems…

Computer Science and Game Theory · Computer Science 2017-12-19 Kiran Sharma , Anamika , Anindya S. Chakrabarti , Anirban Chakraborti , Sujoy Chakravarty

We consider the problem of efficient inference of the Average Treatment Effect in a sequential experiment where the policy governing the assignment of subjects to treatment or control can change over time. We first provide a central limit…

Machine Learning · Statistics 2024-03-05 Thomas Cook , Alan Mishler , Aaditya Ramdas

In this paper, we develop a general law of large numbers and central limit theorem for cumulative reward processes associated with finite state Markov jump processes with non-stationary transition rates. Such models commonly arise in…

Probability · Mathematics 2025-10-15 Monte Fischer , Peter W. Glynn

The stratified resampling mechanism is one of the resampling schemes commonly used in the resampling steps of particle filters. In the present paper, we prove a central limit theorem for this mechanism under the assumption that the initial…

Probability · Mathematics 2023-08-07 Roberta Flenghi , Benjamin Jourdain

Dirichlet process mixture (DPM) models tend to produce many small clusters regardless of whether they are needed to accurately characterize the data - this is particularly true for large data sets. However, interpretability, parsimony, data…

Machine Learning · Computer Science 2018-02-16 Jun Lu , Meng Li , David Dunson

This paper presents some limit theorems for certain functionals of moving averages of semimartingales plus noise which are observed at high frequency. Our method generalizes the pre-averaging approach (see [Bernoulli 15 (2009) 634--658,…

Statistics Theory · Mathematics 2010-10-05 Jean Jacod , Mark Podolskij , Mathias Vetter

We investigate the probability density of rescaled sums of iterates of deterministic dynamical systems, a problem relevant for many complex physical systems consisting of dependent random variables. A Central Limit Theorem (CLT) is only…

Statistical Mechanics · Physics 2007-05-23 Ugur Tirnakli , Christian Beck , Constantino Tsallis

The number of faces of the convex hull of $n$ independent and identically distributed random points chosen on the boundary of a smooth convex body in $\mathbb{R}^d$ is investigated. In dimensions two and three the number of $k$-faces is…

Probability · Mathematics 2025-09-25 Matthias Reitzner , Mathias Sonnleitner

Recently there has been significant interest in constructing ordered analogues of Petrov's two-parameter extension of Ethier and Kurtz's infinitely-many-neutral-alleles diffusion model. One method for constructing these processes goes…

Probability · Mathematics 2022-02-09 Kelvin Rivera-Lopez , Douglas Rizzolo

The classical $k$-means algorithm for partitioning $n$ points in $\mathbb{R}^d$ into $k$ clusters is one of the most popular and widely spread clustering methods. The need to respect prescribed lower bounds on the cluster sizes has been…

Optimization and Control · Mathematics 2016-08-04 Steffen Borgwardt , Andreas Brieden , Peter Gritzmann

In this paper, we investigate the effect of data heterogeneity across clients on the performance of distributed learning systems, i.e., one-round Federated Learning, as measured by the associated generalization error. Specifically, $K$…

Machine Learning · Computer Science 2025-05-21 Masoud Kavian , Romain Chor , Milad Sefidgaran , Abdellatif Zaidi
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