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In this paper, we prove a mimicking theorem for stochastic processes with an additive Gaussian noise along with some entropy and transport type estimates. As an application of these results, we prove sharp quantitative propagation of chaos…

Probability · Mathematics 2024-05-15 Kevin Hu , Kavita Ramanan , William Salkeld

In this paper the numerical approximation of stochastic differential equations satisfying a global monotonicity condition is studied. The strong rate of convergence with respect to the mean square norm is determined to be $\frac{1}{2}$ for…

Numerical Analysis · Mathematics 2017-09-01 Adam Andersson , Raphael Kruse

We approximate the solution of some linear systems of SDEs driven by a fractional Brownian motion $B^H$ with Hurst parameter $H\in(\frac{1}{2},1)$ in the Wick--It\^{o} sense, including a geometric fractional Brownian motion. To this end, we…

Statistics Theory · Mathematics 2010-10-11 Christian Bender , Peter Parczewski

We discuss maximum likelihood estimation of parameters for models governed by a stochastic differential equation driven by a mixed fractional Brownian motion with random effects.

Probability · Mathematics 2021-05-03 B. L. S. Prakasa Rao

We study the relationship between mixed stochastic differential equations and the corresponding rough path equations driven by standard Brownian motion and fractional Brownian motion with Hurst parameter $H>1/2$. We establish a correction…

Probability · Mathematics 2015-04-28 Andreas Neuenkirch , Taras Shalaiko

Stochastic averaging for a class of stochastic differential equations (SDEs) with fractional Brownian motion, of the Hurst parameter H in the interval (1/2, 1), is investigated. An averaged SDE for the original SDE is proposed, and their…

Dynamical Systems · Mathematics 2013-01-22 Yong Xu , Rong Guo , Di Liu , Huiqing Zhang , Jinqiao Duan

This paper deals with the identification of the multivariate fractional Brownian motion, a recently developed extension of the fractional Brownian motion to the multivariate case. This process is a $p$-multivariate self-similar Gaussian…

Statistics Theory · Mathematics 2011-11-16 Pierre-Olivier Amblard , Jean-François Coeurjolly

Fractional Brownian motion, a Gaussian non-Markovian self-similar process with stationary long-correlated increments, has been identified to give rise to the anomalous diffusion behavior in a great variety of physical systems. The…

The stochastic heat equation on the sphere driven by additive isotropic Wiener noise is approximated by a spectral method in space and forward and backward Euler-Maruyama schemes in time. The spectral approximation is based on a truncation…

Numerical Analysis · Mathematics 2024-02-05 Annika Lang , Ioanna Motschan-Armen

In this article, we consider numerical schemes for polynomial diffusions on the unit ball, which are solutions of stochastic differential equations with a diffusion coefficient of the form $\sqrt{1-|x|^{2}}$. We introduce a semi-implicit…

Probability · Mathematics 2022-06-14 Takuya Nakagawa , Dai Taguchi , Tomooki Yuasa

We establish Talagrand's $T_1$ and $T_2$ inequalities for the law of the solution of a stochastic differential equation driven by a fractional Brownian motion with Hurst parameter $H>1/2$. We use the $L^2$ metric and the uniform metric on…

Statistics Theory · Mathematics 2012-03-01 Bruno Saussereau

We investigate the fractional Hardy-H\'enon equation with fractional Brownian noise $$ \partial_tu(t)+(-\Delta)^{\theta/2} u(t)=|x|^{-\gamma} |u(t)|^{p-1}u(t)+\mu \, \partial_t B^H(t), $$ where $\theta>0$, $p>1$, $\gamma\geq 0$, $\mu…

Analysis of PDEs · Mathematics 2025-06-12 R. Alessa , R. Al Subaie , M. Alwohaibi , M. Majdoub , E. Mliki

This work investigates a fully discrete mixed finite element method for the stochastic Boussinesq system driven by multiplicative noise. The spatial discretization is performed using a standard mixed finite element method, while the…

Numerical Analysis · Mathematics 2025-12-25 Liet Vo

We consider the problem of numerically estimating expectations of solutions to stochastic differential equations driven by Brownian motions in the commonly occurring small noise regime. We consider (i) standard Monte Carlo methods combined…

Numerical Analysis · Mathematics 2015-06-08 David F. Anderson , Desmond J. Higham , Yu Sun

We study the numerical approximation of stochastic evolution equations with a monotone drift driven by an infinite-dimensional Wiener process. To discretize the equation, we combine a drift-implicit two-step BDF method for the temporal…

Numerical Analysis · Mathematics 2021-05-20 Raphael Kruse , Rico Weiske

In this paper we study strong approximation of the solution of a scalar stochastic differential equation (SDE) at the final time in the case when the drift coefficient may have discontinuities in space. Recently it has been shown in…

Probability · Mathematics 2019-04-22 Thomas Müller-Gronbach , Larisa Yaroslavtseva

The backward Euler-Maruyama (BEM) method is employed to approximate the invariant measure of stochastic differential equations, where both the drift and the diffusion coefficient are allowed to grow super-linearly. The existence and…

Probability · Mathematics 2022-06-24 Wei Liu , Xuerong Mao , Yue Wu

We introduce the Multilevel Euler-Maruyama (ML-EM) method compute solutions of SDEs and ODEs using a range of approximators $f^1,\dots,f^k$ to the drift $f$ with increasing accuracy and computational cost, only requiring a few evaluations…

Machine Learning · Computer Science 2026-03-26 Arthur Jacot

In this paper, we derive the exact rate of convergence of some approximation schemes associated to scalar stochastic differential equations driven by a fractional Brownian motion with Hurst index H.

Probability · Mathematics 2007-05-23 Andreas Neuenkirch , Ivan Nourdin

In this paper, we establish the strong well-posedness of SDEs with merely integrable time-dependent drifts driven by fractional Brownian motions with Hurst parameter H<1/2. Our result holds over the entire subcritical regime and can be…

Probability · Mathematics 2026-02-26 Jiazhen Gu , Qian Yu
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