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We consider a random walk model in a one-dimensional environment, formed by several zones of finite width with the fixed transition probabilities. It is also assumed that the transitions to the left and right neighboring points have unequal…

Statistical Mechanics · Physics 2017-08-18 A. V. Nazarenko , V. Blavatska

We consider two particles performing continuous-time nearest neighbor random walk on $\mathbb Z$ and interacting with each other when they are at neighboring positions. Typical examples are two particles in the partial exclusion process or…

Probability · Mathematics 2017-12-08 Gioia Carinci , Cristian Giardina , Frank Redig

The integer points (sites) of the real line are marked by the positions of a standard random walk. We say that the set of marked sites is weakly, moderately or strongly sparse depending on whether the jumps of the standard random walk are…

Probability · Mathematics 2019-03-08 Dariusz Buraczewski , Piotr Dyszewski , Alexander Iksanov , Alexander Marynych

In this article we obtain uniform estimates on the absorption of Brownian motion by porous interfaces surrounding a compact set. An important ingredient is the construction of certain resonance sets, which are hard to avoid for Brownian…

Probability · Mathematics 2020-07-08 Maximilian Nitzschner , Alain-Sol Sznitman

The food web is a directed graph in which nodes label species and directed links represent the predation between species. The cascade model generates random food webs. The continuum cascade model is a Poisson approximation of the cascade…

Probability · Mathematics 2018-08-03 Yoshiaki Itoh

In elections, the vote shares or turnout rates show a strong spatial correlation. The logarithmic decay with distance suggests that a 2D noisy diffusive equation describes the system. Based on the study of U.S. presidential elections data,…

Physics and Society · Physics 2019-05-29 Shintaro Mori , Masato Hisakado , Kazuaki Nakayama

We make the first steps towards generalizing the theory of stochastic block models, in the sparse regime, towards a model where the discrete community structure is replaced by an underlying geometry. We consider a geometric random graph…

Machine Learning · Statistics 2022-07-04 Ronen Eldan , Dan Mikulincer , Hester Pieters

Bayesian Neural Networks provide a principled framework for uncertainty quantification by modeling the posterior distribution of network parameters. However, exact posterior inference is computationally intractable, and widely used…

Machine Learning · Computer Science 2025-12-02 Alfredo Reichlin , Miguel Vasco , Danica Kragic

In this paper, we obtain an explicit representation of the transition density of the one-dimensional skew Brownian motion with (a constant drift and) two semipermeable barriers. Moreover we propose a rejection method to simulate this…

Probability · Mathematics 2015-09-10 David Dereudre , Sara Mazzonetto , Sylvie Roelly

We are given a set of elements in a metric space. The distribution of the elements is arbitrary, possibly adversarial. Can we weigh the elements in a way that is resistant to such (adversarial) manipulations? This problem arises in various…

Machine Learning · Computer Science 2026-02-18 Damien Berriaud , Roger Wattenhofer

In this paper we study a random walk on an affine building of type $\tilde{A}_r$, whose radial part, when suitably normalized, converges to the Brownian motion of the Weyl chamber. This gives a new discrete approximation of this process,…

Probability · Mathematics 2009-05-25 Bruno Schapira

According to the Dudley-Wichura extension of the Skorohod representation theorem, convergence in distribution to a limit in a separable set is equivalent to the existence of a coupling with elements converging a.s. in the metric. A density…

Probability · Mathematics 2015-09-01 Hermann Thorisson

A discrete model of Brownian sticky flows on the unit circle is described: it is constructed with products of Beta matrices on the discrete torus. Sticky flows are defined by their ``moments'' which are consistent systems of transition…

Probability · Mathematics 2007-05-23 Yves Le Jan , Sophie Lemaire

Using a recently developed method to simulate percolation on large clusters of distributed machines [N. R. Moloney and G. Pruessner, Phys. Rev. E 67, 037701 (2003)], we have numerically calculated crossing, spanning and wrapping…

Statistical Mechanics · Physics 2007-05-23 Gunnar Pruessner , Nicholas R. Moloney

We examine a new path transform on 1-dimensional simple random walks and Brownian motion, the quantile transform. This transformation relates to identities in fluctuation theory due to Wendel, Port, Dassios and others, and to discrete and…

Probability · Mathematics 2015-09-21 Sami Assaf , Noah Forman , Jim Pitman

Stochastic models of varying complexity have been proposed to describe the dispersion of particles in turbulent flows, from simple Brownian motion to complex temporally and spatially correlated models. A method is needed to compare…

Fluid Dynamics · Physics 2022-07-13 Martin T. Brolly , James R. Maddison , Aretha L. Teckentrup , Jacques Vanneste

Diffusion is a key element of a large set of phenomena occurring on natural and social systems modeled in terms of complex weighted networks. Here, we introduce a general formalism that allows to easily write down mean-field equations for…

Statistical Mechanics · Physics 2010-07-14 Andrea Baronchelli , Romualdo Pastor-Satorras

In recent years, several experiments highlighted a new type of diffusion anomaly, which was called Brownian yet non-Gaussian diffusion. In systems displaying this behavior, the mean squared displacement of the diffusing particles grows…

Statistical Mechanics · Physics 2023-08-01 Adrian Pacheco-Pozo , Igor M. Sokolov

Diffusive transport in many complex systems features a crossover between anomalous diffusion at short times and normal diffusion at long times. This behavior can be mathematically modeled by cutting off (tempering) beyond a mesoscopic…

Statistical Mechanics · Physics 2021-10-15 Thomas Vojta , Zachary Miller , Samuel Halladay

Persistent homology is a multiscale method for analyzing the shape of sets and functions from point cloud data arising from an unknown distribution supported on those sets. When the size of the sample is large, direct computation of the…

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