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Birth-death processes form a natural class where ideas and results on large deviations can be tested. In this paper, we derive a large deviation principle under the assumption that the rate of a jump down (death) is growing asymptotically…

Probability · Mathematics 2023-08-21 N. D. Vvedenskaya , A. V. Logachov , Y. M. Suhov , A. A. Yambartsev

We consider the weakly asymmetric exclusion process on the $d$-dimensional torus. We prove a large deviations principle for the time averaged empirical density and current in the joint limit in which both the time interval and the number of…

Probability · Mathematics 2021-11-12 Lorenzo Bertini , Davide Gabrielli , Claudio Landim

We prove a convergence theorem for a sequence of super-Brownian motions moving among hard Poissonian obstacles, when the intensity of the obstacles grows to infinity but their diameters shrink to zero in an appropriate manner. The…

Probability · Mathematics 2009-06-10 Amandine Veber

This paper studies probabilistic mean-field models for interacting bosons at a positive temperature in the thermodynamic limit with random particle density. In particular, we prove large deviation principles for empirical cycle counts in…

Probability · Mathematics 2018-09-11 Stefan Adams , Matthew Dickson

We consider a class of slow-fast processes on a connected complete Riemannian manifold $M$.The limiting dynamics as the scale separation goes to $\infty$ is governed by the averaging principle. Around this limit, we prove large deviation…

Probability · Mathematics 2024-03-11 Yanyan Hu , Richard C. Kraaij , Fubao Xi

Project a collection of points on the high-dimensional sphere onto a random direction. If most of the points are sufficiently far from one another in an appropriate sense, the projection is locally close in distribution to the Poisson point…

Probability · Mathematics 2011-06-27 Itai Benjamini , Oded Schramm , Sasha Sodin

This paper develops a new technique for the path approximation of one-dimensional stochastic processes, more precisely the Brownian motion and families of stochastic differential equations sharply linked to the Brownian motion (usually…

Probability · Mathematics 2020-12-16 Madalina Deaconu , Samuel Herrmann

Time-irreversible stochastic processes are frequently used in natural sciences to explain non-equilibrium phenomena and to design efficient stochastic algorithms. Our main goal in this thesis is to analyse their dynamics by means of large…

Probability · Mathematics 2021-09-21 Mikola C. Schlottke

We study determinantal random point processes on a compact complex manifold X associated to an Hermitian metric on a line bundle over X and a probability measure on X. Physically, this setup describes a free fermion gas on X subject to a…

Complex Variables · Mathematics 2011-06-27 Robert J. Berman

We show that the random point measures induced by vertices in the convex hull of a Poisson sample on the unit ball, when properly scaled and centered, converge to those of a mean zero Gaussian field. We establish limiting variance and…

Probability · Mathematics 2008-01-09 T. Schreiber , J. E. Yukich

In the framework of Harnack type Dirichlet forms, we prove a large deviation principle for the asymptotics of reversible Markov processes with rate function given by the energy of the paths.

Probability · Mathematics 2009-07-28 Ann-Kathrin Jarecki

Given $\alpha \in (0, \infty)$ and $r \in (0, \infty)$, let ${\cal D}_{r, \alpha}$ be the disc of radius $r$ in the hyperbolic plane having curvature $-\alpha^2$. Consider the Poisson point process having uniform intensity density on ${\cal…

Probability · Mathematics 2021-01-01 Nikolaos Fountoulakis , Joseph Yukich

We establish a large deviation principle for the process of the largest eigenvalue of an Hermitian Brownian motion. By a contraction principle, we recover the LDP for the largest eigenvalue of a rank one deformation of the GUE.

Probability · Mathematics 2012-11-13 Catherine Donati-Martin , Mylène Maïda

We prove Poisson approximation results for the bottom part of the length spectrum of a random closed hyperbolic surface of large genus. Here, a random hyperbolic surface is a surface picked at random using the Weil-Petersson volume form on…

Geometric Topology · Mathematics 2021-03-18 Maryam Mirzakhani , Bram Petri

We prove the large deviation principle for the trajectory of a broad class of mean field interacting Markov jump processes via a general analytic approach based on viscosity solutions. Examples include generalized Ehrenfest models as well…

Probability · Mathematics 2016-06-24 Richard Kraaij

Let $X_1, \ldots, X_n$ be independent random points drawn from an absolutely continuous probability measure with density $f$ in $\mathbb{R}^d$. Under mild conditions on $f$, we derive a Poisson limit theorem for the number of large…

Probability · Mathematics 2018-11-20 László Györfi , Norbert Henze , Harro Walk

Brenier and Grenier [SIAM J. Numer. Anal., 1998] proved that sticky particle dynamics with a large number of particles allow to approximate the entropy solution to scalar one-dimensional conservation laws with monotonic initial data. In…

Analysis of PDEs · Mathematics 2015-11-11 Benjamin Jourdain , Julien Reygner

We establish, under the Cramer exponential moment condition in a neighbourhood of zero, the Extended Large Deviation Principle for the Random Walk and the Compound Poisson processes in the metric space $\V$ of functions of finite variation…

Probability · Mathematics 2016-11-01 F. C. Klebaner , A. A. Mogulskii

We establish a sharp large deviation principle for renewal-reward processes, supposing that each renewal involves a broad-sense reward taking values in a real separable Banach space. In fact, we demonstrate a weak large deviation principle…

Probability · Mathematics 2023-04-24 Marco Zamparo

In this paper we consider the problem of estimating the parameters of a Poisson arrival process where the rate function is assumed to lie in the span of a known basis. Our goal is to estimate the basis expansions coefficients given a…

Information Theory · Computer Science 2018-12-24 Michael G. Moore , Mark A. Davenport