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An urn contains balls of d colors. At each time, a ball is drawn and then replaced together with a random number of balls of the same color. Assuming that some colors are dominated by others, we prove central limit theorems. Some…

Probability · Mathematics 2009-07-06 Patrizia Berti , Irene Crimaldi , Luca Pratelli , Pietro Rigo

We consider a variant of the randomly reinforced urn where more balls can be simultaneously drawn out and balls of different colors can be simultaneously added. More precisely, at each time-step, the conditional distribution of the number…

Probability · Mathematics 2015-07-09 Irene Crimaldi

We prove a Central Limit Theorem for the sequence of random compositions of a two-color randomly reinforced urn. As a consequence, we are able to show that the distribution of the urn limit composition has no point masses.

Probability · Mathematics 2012-11-27 G. Aletti , C. May , P. Secchi

We take a unified approach to central limit theorems for a class of irreducible urn models with constant replacement matrix. Depending on the eigenvalue, we consider appropriate linear combinations of the number of balls of different…

Probability · Mathematics 2008-05-29 Gopal K. Basak , Amites Dasgupta

The generalized P\`olya urn (GPU) models and their variants have been investigated in several disciplines. However, typical assumptions made with respect to the GPU do not include urn models with diagonal replacement matrix, which arise in…

Probability · Mathematics 2015-02-24 Andrea Ghiglietti , Anand N. Vidyashankar , William F. Rosenberger

We complete the study of the model introduced in [11]. It is a two-color urn model with multiple drawing and random (non-balanced) time-dependent reinforcement matrix. The number of sampled balls at each time-step is random. We identify the…

Statistics Theory · Mathematics 2022-08-05 Irene Crimaldi , Pierre-Yves Louis , Ida G. Minelli

We construct an independent increments Gaussian process associated to a class of multicolor urn models. The construction uses random variables from the urn model which are different from the random variables for which central limit theorems…

Probability · Mathematics 2007-05-23 Gopal K Basak , Amites Dasgupta

This paper considers a two-color, single-draw urn model with two types of balls, denoted type $1$ and type $2$, with initial counts $Y^1_0\in N^+$ and $Y^2_0\in N^+$, respectively. At each discrete time step, a ball is drawn uniformly at…

Probability · Mathematics 2026-05-27 Jianan Shi , Qing Yin , Yu Miao

The purpose of this work is to establish a central limit theorem that can be applied to a particular form of Markov chains, including the number of descents in a random permutation of $\mathfrak{S}_n$, two-type generalized P{\'o}lya urns,…

Probability · Mathematics 2021-06-09 Olivier Garet

Using an averaged generating function for coloured hard-dimers, some random variables of interest are studied. The main result lies in the fact that all their probability distributions obey a central limit theorem.

Probability · Mathematics 2009-06-22 Maria Simonetta Bernabei , Horst Thaler

For the plain Polya urn with two colors, black and white, we prove a functional central limit theorem for the number of white balls assuming that the initial number of black balls is large. Depending on the initial number of white balls,…

Probability · Mathematics 2019-06-03 Dimitris Cheliotis , Dimitra Kouloumpou

A Central Limit Theorem is proved for linear random fields when sums are taken over finite disjoint union of rectangles. The approach does not rely upon the use of Beveridge Nelson decomposition and the conditions needed are similar to…

Probability · Mathematics 2010-07-14 Atul Mallik , Michael Woodroofe

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

Recently a new type of central limit theorem for belief functions was given in Epstein et al. [9]. In this paper, we generalize the central limit theorem in Epstein et al. [9] to accommodate general bounded random variables. These results…

Probability · Mathematics 2017-12-21 Xiaomin Shi

We show how a central limit theorem for Poisson model random polygons implies a central limit theorem for uniform model random polygons. To prove this implication, it suffices to show that in the two models, the variables in question have…

Probability · Mathematics 2012-08-14 John Pardon

We prove a central limit theorem applicable to one dimensional stochastic approximation algorithms that converge to a point where the error terms of the algorithm do not vanish. We show how this applies to a certain class of these…

Probability · Mathematics 2011-02-24 Henrik Renlund

We prove a central limit theorem for a sequence of random variables whose means are ambiguous and vary in an unstructured way. Their joint distribution is described by a set of measures. The limit is (not the normal distribution and is)…

Probability · Mathematics 2020-07-01 Zengjing Chen , Larry G. Epstein

Local limit theorems are derived for the number of occupied urns in general finite and infinite urn models under the minimum condition that the variance tends to infinity. Our results represent an optimal improvement over previous ones for…

Probability · Mathematics 2009-09-29 Hsien-Kuei Hwang , Svante Janson

This is a research endeavor in two parts. We study a class of balanced urn schemes on balls of two colours (say white and black). At each drawing, a sample of size $m\ge 1$ is drawn from the urn, and ball addition rules are applied. We…

Probability · Mathematics 2015-04-01 Markus Kuba , Hosam M. Mahmoud

Sanov's Theorem and the Conditional Limit Theorem (CoLT) are established for a multicolor Polya Eggenberger urn sampling scheme, giving the Polya divergence and the Polya extension to the Maximum Relative Entropy (MaxEnt) method. Polya…

Statistical Mechanics · Physics 2007-05-23 Marian Grendar , Robert K. Niven
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