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Two families of stochastic interacting particle systems, the interacting Brownian motions and Bessel processes, are defined as extensions of Dyson's Brownian motion models and the eigenvalue processes of the Wishart and Laguerre processes…

Mathematical Physics · Physics 2014-06-09 Sergio Andraus

It has been known for a long time that for birth-and-death processes started in zero the first passage time of a given level is distributed as a sum of independent exponentially distributed random variables, the parameters of which are the…

Probability · Mathematics 2010-12-22 Jan M. Swart

We present a straightforward discretization of the Bessel functions $J_n(x)$ to discrete counterparts $B^{(N)}_n(x_m)$, of $N$ integer orders $n$ on $N$ integer points $x_m \equiv m$, that we call discrete Bessel functions. These are built…

Mathematical Physics · Physics 2026-04-30 Kenan Uriostegui , Kurt Bernardo Wolf

The birth-death process is a special type of continuous-time Markov chain with index set $\mathbb{N}$. Its resolvent matrix can be fully characterized by a set of parameters $(\gamma, \beta, \nu)$, where $\gamma$ and $\beta$ are…

Probability · Mathematics 2024-09-10 Liping Li

Multivariate Bessel processes are classified via associated root systems and positive multiplicity constants. They describe the dynamics of interacting particle systems of Calogero-Moser-Sutherland type. Recently, Andraus, Katori, and…

Probability · Mathematics 2020-09-30 Michael Voit

We establish connections between the absorption probabilities of a class of birth-death processes with killing, and the stationary tail of a related class of birth-death processes with catastrophes. The major ingredients of the proofs are a…

Probability · Mathematics 2026-01-28 Ellen Baake , Fernando Cordero , Enrico Di Gaspero , Anton Wakolbinger

Differential calculus on discrete sets is developed in the spirit of noncommutative geometry. Any differential algebra on a discrete set can be regarded as a `reduction' of the `universal differential algebra' and this allows a systematic…

High Energy Physics - Theory · Physics 2009-10-28 A. Dimakis , F. Müller-Hoissen

This paper establishes the global asymptotic equivalence, in the sense of the Le Cam $\Delta$-distance, between scalar diffusion models with unknown drift function and small variance on the one side, and nonparametric autoregressive models…

Probability · Mathematics 2015-03-05 Ester Mariucci

Given a birth-death process on $\mathbb {N}$ with semigroup $(P_t)_{t\geq0}$ and a discrete gradient ${\partial}_u$ depending on a positive weight $u$, we establish intertwining relations of the form ${\partial}_uP_t=Q_t\,{\partial}_u$,…

Probability · Mathematics 2013-12-12 Djalil Chafaï , Aldéric Joulin

Diffusion processes are a class of stochastic differential equations (SDEs) providing a rich family of expressive models that arise naturally in dynamic modelling tasks. Probabilistic inference and learning under generative models with…

Machine Learning · Computer Science 2024-02-28 Prakhar Verma , Vincent Adam , Arno Solin

We derive a self-duality relation for a one-dimensional model of branching and annihilating random walkers with an even number of offsprings. With the duality relation and by deriving exact results in some limiting cases involving fast…

Statistical Mechanics · Physics 2009-10-31 K. Mussawisade , J. E. Santos , G. M. Schütz , ;

We define a graph process $\mathcal{G}(p,q)$ based on a discrete branching process with deletions and mergers, which is inspired by the 4-cycle structure of both the hypercube $Q_d$ and the lattice $\mathbb{Z}^d$ for large $d$. Individuals…

Probability · Mathematics 2021-04-12 Laura Eslava , Sarah Penington , Fiona Skerman

A new stochastic process is introduced and considered - squared Bessel process with special stochastic time. The analogues of fundamental properties for Brownian motion are deduced for squared Bessel process. In particular an analogue of…

Probability · Mathematics 2014-10-14 Maciej Wiśniewolski

In this work we introduce the discrete-space broken line process (with discrete and continues parameter values) and derive some of its properties. We explore polygonal Markov fields techniques developed by Arak-Surgailis. The discrete…

Probability · Mathematics 2017-02-20 Leonardo T. Rolla , Vladas Sidoravicius , Donatas Surgailis , Maria E. Vares

Branching processes form an important family of stochastic processes that have been successfully applied in many fields. In this paper, we focus our attention on controlled multi-type branching processes (CMBPs). A Feller-type diffusion…

Probability · Mathematics 2024-05-13 Matyas Barczy , Miguel González , Pedro Martín-Chávez , Inés del Puerto

We consider Markov processes that alternate continuous motions and jumps in a general locally compact polish space. Starting from a mechanistic construction, a first contribution of this article is to provide conditions on the dynamics so…

Probability · Mathematics 2022-04-07 Ronan Le Guével , Frédéric Lavancier , Emilien Manent

Whilst deep neural networks have shown great empirical success, there is still much work to be done to understand their theoretical properties. In this paper, we study the relationship between random, wide, fully connected, feedforward…

Machine Learning · Statistics 2018-08-17 Alexander G. de G. Matthews , Mark Rowland , Jiri Hron , Richard E. Turner , Zoubin Ghahramani

We consider the Busemann process in planar directed first passage percolation. We extend existing techniques to establish the existence of the process in our setting and determine its distribution in a number of integrable models. As…

Probability · Mathematics 2025-10-23 Sam McKeown

We consider families of E_0-semigroups continuously parametrized by a compact Hausdorff space, which are cocycle-equivalent to a given E_0-semigroup \beta. When the gauge group of $\beta$ is a Lie group, we establish a correspondence…

Operator Algebras · Mathematics 2011-06-30 Ilan Hirshberg , Daniel Markiewicz

Despite Flow Matching and diffusion models having emerged as powerful generative paradigms for continuous variables such as images and videos, their application to high-dimensional discrete data, such as language, is still limited. In this…

Machine Learning · Computer Science 2024-11-06 Itai Gat , Tal Remez , Neta Shaul , Felix Kreuk , Ricky T. Q. Chen , Gabriel Synnaeve , Yossi Adi , Yaron Lipman