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We show in detail some results, outlined in a previous paper regarding the case of Brownian motion (BM), about the distribution of the $n$th-passage time of a one-dimensional diffusion obtained by a space or time transformation of BM,…

Probability · Mathematics 2018-04-12 Mario Abundo , Maria Beatrice Scioscia Santoro

We show that all the time-dependent statistical properties of the rightmost points of a branching Brownian motion can be extracted from the traveling wave solutions of the Fisher-KPP equation. We show that the distribution of all the…

Statistical Mechanics · Physics 2015-05-20 Éric Brunet , Bernard Derrida

We study a Schilder-type large deviation principle for sticky-reflected Brownian motion with boundary diffusion, both at the static and sample path level in the short-time limit. A sharp transition for the rate function occurs, depending on…

Analysis of PDEs · Mathematics 2025-01-22 Jean-Baptiste Casteras , Leonard Monsaingeon , Luca Nenna

We provide an overview of some recent techniques involving the Malliavin calculus of variations and the so-called ``Stein's method'' for the Gaussian approximations of probability distributions. Special attention is devoted to establishing…

Probability · Mathematics 2009-09-17 Ivan Nourdin , Giovanni Peccati

We consider n-point sticky Brownian motions: a family of n diffusions that evolve as independent Brownian motions when they are apart, and interact locally so that the set of coincidence times has positive Lebesgue measure with positive…

Probability · Mathematics 2020-10-09 Guillaume Barraquand , Mark Rychnovsky

We study systems of interacting Brownian particles in one dimension constructed as the diffusion scaling limits of Fisher's vicious walk models. We define two types of nonintersecting Brownian motions, in which we impose no condition (resp.…

Statistical Mechanics · Physics 2007-05-23 M. Katori , H. Tanemura

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

The movement of a particle described by Brownian motion is quantified by a single parameter, $D$, the diffusion constant. The estimation of $D$ from a discrete sequence of noisy observations is a fundamental problem in biological single…

Subcellular Processes · Quantitative Biology 2016-04-13 Peter K. Relich , Mark J. Olah , Patrick J. Cutler , Keith A. Lidke

Herein we develop a dynamical foundation for fractional Brownian Motion. A clear relation is established between the asymptotic behaviour of the correlation function and diffusion in a dynamical system. Then, assuming that scaling is…

chao-dyn · Physics 2008-02-03 R Mannella , P Grigolini , BJ West

One century after Einstein's work, Brownian Motion still remains both a fundamental open issue and a continous source of inspiration for many areas of natural sciences. We first present a discussion about stochastic and deterministic…

Chaotic Dynamics · Physics 2009-11-10 Fabio Cecconi , Massimo Cencini , Massimo Falcioni , Angelo Vulpiani

In this article, we obtain, for the total variance distance, the error bounds between Poisson and convolution of power series distributions via Stein's method. This provides a unified approach to many known discrete distributions. Several…

Probability · Mathematics 2020-06-26 A. N. Kumar , P. Vellaisamy , F. Viens

We derive and analyze new diffusion approximations of stationary distributions of Markov chains that are based on second- and higher-order terms in the expansion of the Markov chain generator. Our approximations achieve a higher degree of…

Probability · Mathematics 2022-07-12 Anton Braverman , J. G. Dai , Xiao Fang

The Arcsine laws of Brownian motion are a collection of results describing three different statistical quantities of one-dimensional Brownian motion: the time at which the process reaches its maximum position, the total time the process…

Statistical Mechanics · Physics 2023-08-03 Toby Kay , Luca Giuggioli

We derive diffusion constants and martingales for senile random walks with the help of a time-change. We provide direct computations of the diffusion constants for the time-changed walks. Alternatively, the values of these constants can be…

Probability · Mathematics 2007-11-19 Wouter Kager

In this paper we study the discrete approximation to Brownian motion with varying dimension (BMVD in abbreviation) introduced in [4] by continuous time random walks on square lattices. The state space of BMVD contains a $2$-dimensional…

Probability · Mathematics 2021-10-26 Shuwen Lou

By a method inspired of the Stein's method, we derive an upper-bound of the Rubinstein distance between two absolutely continuous probability measures on configurations space. As an application, we show that the best way to approximate a…

Probability · Mathematics 2007-07-04 Laurent Decreusefond , Nicolas Savy

We study a model of diffusion in a brownian potential. This model was firstly introduced by T. Brox (1986) as a continuous time analogue of random walk in random environment. We estimate the deviations of this process above or under its…

Probability · Mathematics 2011-09-06 Gabriel Faraud

Anomalous diffusion is frequently described by scaled Brownian motion (SBM), a Gaussian process with a power-law time dependent diffusion coefficient. Its mean squared displacement is $\langle x^2(t)\rangle\simeq\mathscr{K}(t)t$ with…

Statistical Mechanics · Physics 2014-12-24 J. -H. Jeon , A. V. Chechkin , R. Metzler

Upon almost-every realisation of the Brownian continuum random tree (CRT), it is possible to define a canonical diffusion process or `Brownian motion'. The main result of this article establishes that the cover time of the Brownian motion…

Probability · Mathematics 2025-09-30 George Andriopoulos , David A. Croydon , Vlad Margarint , Laurent Menard

This paper provides an introduction to the Stein method framework in the context of steady-state diffusion approximations. The framework consists of three components: the Poisson equation and gradient bounds, generator coupling, and moment…

Probability · Mathematics 2017-02-21 Anton Braverman , J. G. Dai , Jiekun Feng