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The infinitely-many-neutral-alleles model has recently been extended to a class of diffusion processes associated with Gibbs partitions of two-parameter Poisson-Dirichlet type. This paper introduces a family of infinite-dimensional…

Probability · Mathematics 2013-02-15 Matteo Ruggiero , Stephen G. Walker , Stefano Favaro

This paper introduces a rigorous framework for defining generative diffusion models in infinite dimensions via Doob's h-transform. Rather than relying on time reversal of a noising process, a reference diffusion is forced towards the target…

Machine Learning · Statistics 2026-02-09 Thorben Pieper-Sethmacher , Daniel Paulin

We introduce a class of probability measure-valued diffusions, coined polynomial, of which the well-known Fleming--Viot process is a particular example. The defining property of finite dimensional polynomial processes considered by Cuchiero…

Probability · Mathematics 2018-07-10 Christa Cuchiero , Martin Larsson , Sara Svaluto-Ferro

We consider an infinite dimensional diffusion on $T^{\mathbb Z^d}$, where $T$ is the circle, defined by an infinitesimal generator of the form $L=\sum_{i\in\mathbb Z^d}\left(\frac{a_i(\eta)}{2}\partial^2_i +b_i(\eta)\partial_i\right)$, with…

Probability · Mathematics 2016-08-08 Alejandro F. Ramirez

The large-time asymptotics of the solutions to a class of degenerate parabolic cross-diffusion systems is analyzed. The equations model the interaction of an arbitrary number of population species in a bounded domain with no-flux boundary…

Analysis of PDEs · Mathematics 2023-07-03 Xiuqing Chen , Ansgar Jüngel , Xi Lin , Ling Liu

The Wright-Fisher family of diffusion processes is a widely used class of evolutionary models. However, simulation is difficult because there is no known closed-form formula for its transition function. In this article we demonstrate that…

Methodology · Statistics 2023-10-02 Paul A. Jenkins , Dario Spano

The entropy production rate is a central quantity in non-equilibrium statistical physics, scoring how far a stochastic process is from being time-reversible. In this paper, we compute the entropy production of diffusion processes at…

Statistical Mechanics · Physics 2023-06-19 Lancelot Da Costa , Grigorios A. Pavliotis

This article considers a class of metastable non-reversible diffusion processes whose invariant measure is a Gibbs measure associated with a Morse potential. In a companion paper [32], we proved the Eyring-Kramers formula for the…

Probability · Mathematics 2022-07-20 Jungkyoung Lee , Insuk Seo

Diffusion models have had a profound impact on many application areas, including those where data are intrinsically infinite-dimensional, such as images or time series. The standard approach is first to discretize and then to apply…

Machine Learning · Statistics 2025-06-09 Jakiw Pidstrigach , Youssef Marzouk , Sebastian Reich , Sven Wang

A homogenization problem of infinite dimensional diffusion processes indexed by ${\mathbf Z}^d$ having periodic drift coefficients is considered. By an application of the uniform ergodic theorem for infinite dimensional diffusion processes…

Probability · Mathematics 2026-03-31 Sergio Albeverio , Michael Rockner , Simonetta Bernabei , Minoru W. Yoshida

We construct a new class of infinite-dimensional diffusions taking values in a generalized Kingman simplex. Our model describes the temporal evolution of the relative frequencies of infinitely-many types which are "labeled" by an arbitrary…

Probability · Mathematics 2026-02-25 Cristina Costantini , Matteo Ruggiero

In the present work we study self-interacting diffusions following an infinite dimensional approach. First we prove existence and uniqueness of a solution with Markov property. Then we study the corresponding transition semigroup and, more…

Probability · Mathematics 2016-04-29 Michel Benaim , Ioana Ciotir , Carl-Erik Gauthier

This paper introduces $\infty$-Diff, a generative diffusion model defined in an infinite-dimensional Hilbert space, which can model infinite resolution data. By training on randomly sampled subsets of coordinates and denoising content only…

Machine Learning · Computer Science 2024-03-04 Sam Bond-Taylor , Chris G. Willcocks

We study a reaction-diffusion system on the real line, where the reactions of the species are given by one reversible reaction according to the mass-action law. We describe different positive limits at both sides of infinity and investigate…

Analysis of PDEs · Mathematics 2023-04-07 Alexander Mielke , Stefanie Schindler

Given any closed Riemannian manifold $M$, we construct a reversible diffusion process on the space ${\mathcal P}(M)$ of probability measures on $M$ that is (i) reversible w.r.t.~the entropic measure ${\mathbb P}^\beta$ on ${\mathcal P}(M)$,…

Probability · Mathematics 2024-04-25 Karl-Theodor Sturm

Recently, many classes of infinitely divisible distributions on R^d have been characterized in several ways. Among others, the first way is to use Levy measures, the second one is to use transformations of Levy measures, and the third one…

Probability · Mathematics 2009-09-11 Takahiro Aoyama , Alexander Lindner , Makoto Maejima

A number of discrete time, finite population size models in genetics describing the dynamics of allele frequencies are known to converge (subject to suitable scaling) to a diffusion process in the infinite population limit, termed the…

Probability · Mathematics 2021-09-14 Jaromir Sant , Paul A. Jenkins , Jere Koskela , Dario Spano

We show the relation between processes which are modeled by a Langevin equation with multiplicative noise and infinite ergodic theory. We concentrate on a spatially dependent diffusion coefficient that behaves as ${D(x)}\sim…

Statistical Mechanics · Physics 2019-05-01 N. Leibovich , E. Barkai

This paper studies new classes of infinitely divisible distributions on R^d. Firstly, the connecting classes with a continuous parameter between the Jurek class and the class of selfdecomposable distributions are revisited. Secondly, the…

Probability · Mathematics 2009-09-11 Makoto Maejima , Muneya Matsui , Mayo Suzuki

Generative diffusion models are extensively used in unsupervised and self-supervised machine learning with the aim to generate new samples from a probability distribution estimated with a set of known samples. They have demonstrated…

Fluid Dynamics · Physics 2026-01-28 Wilfried Genuist , Éric Savin , Filippo Gatti , Didier Clouteau
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