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In this work, we investigate the existence and properties of Gaussian-like densities for weak solutions of multidimensional stochastic differential equations driven by a mixture of completely correlated fractional Brownian motions. We…

Probability · Mathematics 2025-03-06 Maximilian Buthenhoff , Ercan Sönmez

This article deals with the weak errors for averaging principle for a stochastic wave equation in a bounded interval $[0,L]$, perturbed by a oscillating term arising as the solution of a stochastic reaction-diffusion equation evolving with…

Probability · Mathematics 2017-03-20 Hongbo Fu , Li Wan , Jicheng Liu , Xianming Liu

Stochastic approximation is a framework unifying many random iterative algorithms occurring in a diverse range of applications. The stability of the process is often difficult to verify in practical applications and the process may even be…

Probability · Mathematics 2014-03-10 Christophe Andrieu , Matti Vihola

We study the error of the Euler scheme applied to a stochastic partial differential equation. We prove that as it is often the case, the weak order of convergence is twice the strong order. A key ingredient in our proof is Malliavin…

Numerical Analysis · Mathematics 2008-12-18 Arnaud Debussche

We propose a simple stochastic process for modeling improper or noncircular complex-valued signals. The process is a natural extension of a complex-valued autoregressive process, extended to include a widely linear autoregressive term. This…

Methodology · Statistics 2017-03-16 Adam M. Sykulski , Sofia C. Olhede , Jonathan M. Lilly

In many contemporary optimization problems such as those arising in machine learning, it can be computationally challenging or even infeasible to evaluate an entire function or its derivatives. This motivates the use of stochastic…

Optimization and Control · Mathematics 2021-07-01 El-houcine Bergou , Youssef Diouane , Vladimir Kunc , Vyacheslav Kungurtsev , Clément W. Royer

We discuss applications of a recently developed method for model reduction based on linear response theory of weakly coupled dynamical systems. We apply the weak coupling method to simple stochastic differential equations with slow and fast…

Statistical Mechanics · Physics 2016-12-21 Jeroen Wouters , Stamen I. Dolaptchiev , Valerio Lucarini , Ulrich Achatz

A continuous-time Markov process $X$ can be conditioned to be in a given state at a fixed time $T > 0$ using Doob's $h$-transform. This transform requires the typically intractable transition density of $X$. The effect of the $h$-transform…

Probability · Mathematics 2024-09-16 Marc Corstanje , Frank van der Meulen , Moritz Schauer

The phenomenon of critical slowing down (CSD) has played a key role in the search for reliable precursors of catastrophic regime shifts. This is caused by its presence in a generic class of bifurcating dynamical systems. Simple time-series…

Probability · Mathematics 2026-02-10 Paolo Bernuzzi , Christian Kuehn , Andreas Morr

In this paper, we provide a general framework for investigating McKean-Vlasov stochastic partial differential equations. We first show the existence of weak solutions by combining the localizing approximation, Faedo-Galerkin technique,…

Probability · Mathematics 2025-08-12 Wei Hong , Shihu Li , Wei Liu

Stochastic orders are binary relations defined on probability distributions which capture intuitive notions like being larger or being more variable. This paper introduces stochastic ordering of instantaneous SNRs of fading channels as a…

Information Theory · Computer Science 2016-11-17 Cihan Tepedelenlioglu , Adithya Rajan , Yuan Zhang

Symbolic dynamics has proven to be an invaluable tool in analyzing the mechanisms that lead to unpredictability and random behavior in nonlinear dynamical systems. Surprisingly, a discrete partition of continuous state space can produce a…

Machine Learning · Computer Science 2007-07-13 Christopher C. Strelioff , James P. Crutchfield

This work presents the convergence rate analysis of stochastic variants of the broad class of direct-search methods of directional type. It introduces an algorithm designed to optimize differentiable objective functions $f$ whose values can…

Optimization and Control · Mathematics 2020-03-09 Kwassi Joseph Dzahini

We study the recently introduced Busemann subgradient method due to Goodwin, Lewis, Nicolae and L\'opez-Acedo, extending it to minimize the mean of a stochastic function over general Hadamard spaces. We prove a strong convergence theorem…

Optimization and Control · Mathematics 2026-02-10 Nicholas Pischke

We establish the functional convex order results for two scaled McKean-Vlasov processes $X=(X_{t})_{t\in[0, T]}$ and $Y=(Y_{t})_{t\in[0, T]}$ defined on a filtered probability space $(\Omega, \mathcal{F}, (\mathcal{F}_{t})_{t\geq0},…

Probability · Mathematics 2022-01-06 Yating Liu , Gilles Pagès

A noncommutative Fornasini-Marchesini system (a multi-variable version of a linear system) can be realized within a weak Markov process (a model for quantum evolution). For a discrete time parameter the resulting structure is worked out…

Functional Analysis · Mathematics 2015-05-26 Rolf Gohm

In this paper, we study the diffusion approximation for slow-fast stochastic differential equations with state-dependent switching, where the slow component $X^{\varepsilon}$ is the solution of a stochastic differential equation with…

Probability · Mathematics 2025-03-12 Xiaobin Sun , Jue Wang , Yingchao Xie

Discrete dynamics arise naturally in systems with broken temporal translation symmetry and are typically described by first-order recurrence relations representing classical or quantum Markov chains. When memory effects induced by hidden…

Statistical Mechanics · Physics 2025-10-31 Hugues Meyer , Kay Brandner

We consider a collection of fully coupled weakly interacting diffusion processes moving in a two-scale environment. We study the moderate deviations principle of the empirical distribution of the particles' positions in the combined limit…

Probability · Mathematics 2023-07-17 Zachary Bezemek , Konstantinos Spiliopoulos

This note aims at presenting several new theoretical results for the compound Poisson point process, which follows the work of Zhang \emph{et al.} [Insurance~Math.~Econom.~59(2014), 325-336]. The first part provides a new characterization…

Statistics Theory · Mathematics 2019-12-10 Huiming Zhang , Xiaoxu Wu