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Consider a multidimensional diffusion process $X=\{X\left(t\right) :t\in\lbrack0,1]\}$. Let $\varepsilon>0$ be a \textit{deterministic}, user defined, tolerance error parameter. Under standard regularity conditions on the drift and…

Probability · Mathematics 2016-07-22 Jose Blanchet , Xinyun Chen , Jing Dong

The aim of this paper is to represent any continuous local martingale as an almost sure limit of a nested sequence of simple, symmetric random walks, time changed by a discrete quadratic variation process. One basis of this is a similar…

Probability · Mathematics 2010-08-10 Balazs Szekely , Tamas Szabados

We deal with stochastic differential equations with jumps. In order to obtain an accurate approximation scheme, it is usual to replace the "small jumps" by a Brownian motion. In this paper, we prove that for every fixed time $t$, the…

Probability · Mathematics 2023-10-17 Vlad Bally , Yifeng Qin

We introduce exact methods for the simulation of sample paths of one-dimensional diffusions with a discontinuity in the drift function. Our procedures require the simulation of finite-dimensional candidate draws from probability laws…

Methodology · Statistics 2017-01-24 Omiros Papaspiliopoulos , Gareth O. Roberts , Kasia B. Taylor

A correlated random walk approach to diffusion is applied to the disordered nonoverlapping Lorentz gas. By invoking the Lu-Torquato theory for chord-length distributions in random media [J. Chem. Phys. 98, 6472 (1993)], an analytic…

Statistical Mechanics · Physics 2008-02-16 Artur B. Adib

Efficiency of search for randomly distributed targets is a prominent problem in many branches of the sciences. For the stochastic process of L\'evy walks, a specific range of optimal efficiencies was suggested under variation of search…

Statistical Mechanics · Physics 2021-06-11 S. Mohsen J. Khadem , Sabine H. L. Klapp , Rainer Klages

We consider equidistant approximations of stochastic integrals driven by H\"older continuous Gaussian processes of order $H>\frac12$ with discontinuous integrands involving bounded variation functions. We give exact rate of convergence in…

Probability · Mathematics 2022-09-15 Ehsan Azmoodeh , Pauliina Ilmonen , Nourhan Shafik , Tommi Sottinen , Lauri Viitasaari

We introduce Wilson-It\^o diffusions, a class of random fields on $\mathbb{R}^d$ that change continuously along a scale parameter via a Markovian dynamics with local coefficients. Described via forward-backward stochastic differential…

Probability · Mathematics 2023-07-24 Ismael Bailleul , Ilya Chevyrev , Massimiliano Gubinelli

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

We consider conservative cross-diffusion systems for two species where individual motion rates depend linearly on the local density of the other species. We develop duality estimates and obtain stability and approximation results. We first…

Analysis of PDEs · Mathematics 2024-10-30 Vincent Bansaye , Ayman Moussa , Felipe Muñoz-Hernández

It is known that the slow motion $X^\varepsilon$ in the time-scaled multidimensional averaging setup $\frac {dX^\varepsilon(t)}{dt}=\frac 1\varepsilon B(X^\varepsilon(t),\,\xi(t/\varepsilon^2))+b(X^\varepsilon(t),\,\xi(t/\ve^2)),\, t\in…

Probability · Mathematics 2022-04-26 Yuri Kifer

We establish in this paper the existence of weak solutions of infinite-dimensional shift invariant stochastic differential equations driven by a Brownian term. The drift function is very general, in the sense that it is supposed to be…

Probability · Mathematics 2015-09-01 David Dereudre , Sylvie Roelly

Starting with a Brownian motion, we define and study a novel diffusion process by combining stickiness and oscillation properties. The associated stochastic differential equation, resolvent and semigroup are provided. Also the trivariate…

Probability · Mathematics 2023-02-08 Wajdi Touhami

We deal with stochastic differential equations with jumps. In order to obtain an accurate approximation scheme, it is usual to replace the "small jumps" by a Brownian motion. In this paper, we prove that for every fixed time $t$, the…

Probability · Mathematics 2022-12-15 Vlad Bally , Yifeng Qin

We consider the inverse problem of reconstructing the posterior measure over the trajec- tories of a diffusion process from discrete time observations and continuous time constraints. We cast the problem in a Bayesian framework and derive…

Machine Learning · Statistics 2016-12-21 Botond Cseke , David Schnoerr , Manfred Opper , Guido Sanguinetti

In this paper we propose a new deterministic approximation method, called discretization approximation, for Bayesian computation. Discretization approximation is very simple to understand and to implement, It only requires calculating…

Computation · Statistics 2026-01-13 Shifeng Xiong

Motivated by a problematic coming from mathematical finance, this paper is devoted to existing and additional results of continuity and differentiability of the It\^o map associated to rough differential equations. These regularity results…

Probability · Mathematics 2019-01-16 Nicolas Marie

A system of interacting Brownian particles subject to short-range repulsive potentials is considered. A continuum description in the form of a nonlinear diffusion equation is derived systematically in the dilute limit using the method of…

Statistical Mechanics · Physics 2017-10-12 Maria Bruna , S. Jonathan Chapman , Martin Robinson

We provide an It\^{o}'s formula for stochastic dynamical equation on general time scales. Based on this It\^{o}'s formula we give a closed form expression for stochastic exponential on general time scales. We then demonstrate a Girsanov's…

Probability · Mathematics 2017-03-31 Wenqing Hu

Diffusion approximation provides weak approximation for stochastic gradient descent algorithms in a finite time horizon. In this paper, we introduce new tools motivated by the backward error analysis of numerical stochastic differential…

Machine Learning · Computer Science 2019-09-05 Yuanyuan Feng , Tingran Gao , Lei Li , Jian-Guo Liu , Yulong Lu
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