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Switching dynamical systems provide a powerful, interpretable modeling framework for inference in time-series data in, e.g., the natural sciences or engineering applications. Since many areas, such as biology or discrete-event systems, are…

Machine Learning · Computer Science 2021-09-30 Lukas Köhs , Bastian Alt , Heinz Koeppl

For a stopped diffusion process in a multidimensional time-dependent domain $\D$, we propose and analyse a new procedure consisting in simulating the process with an Euler scheme with step size $\Delta$ and stopping it at discrete times…

Probability · Mathematics 2010-04-22 Emmanuel Gobet , Stéphane Menozzi

The purpose of the article is twofold. Firstly, we review some recent results on the maximum likelihood estimation in the regression model of the form $X_t = \theta G(t) + B_t$, where $B$ is a Gaussian process, $G(t)$ is a known function,…

Probability · Mathematics 2018-12-27 Yuliya Mishura , Kostiantyn Ralchenko , Sergiy Shklyar

In many applications, it is often necessary to sample the mean value of certain quantity with respect to a probability measure {\mu} on the level set of a smooth function $\xi: \mathbb{R}^d\rightarrow \mathbb{R}^k$, $1\le k < d$. A…

Probability · Mathematics 2019-09-25 Wei Zhang

Path-wise observables--functionals of stochastic trajectories--are at the heart of time-average statistical mechanics and are central to thermodynamic inequalities such as uncertainty relations, speed limits, and correlation-bounds. They…

Statistical Mechanics · Physics 2026-04-21 Lars Torbjørn Stutzer , Cai Dieball , Aljaž Godec

Penalized estimation methods for diffusion processes and dependent data have recently gained significant attention due to their effectiveness in handling high-dimensional stochastic systems. In this work, we introduce an adaptive…

Statistics Theory · Mathematics 2024-12-24 Alessandro De Gregorio , Dario Frisardi , Francesco Iafrate , Stefano Iacus

It is common practice to treat small jumps of L\'evy processes as Wiener noise and thus to approximate its marginals by a Gaussian distribution. However, results that allow to quantify the goodness of this approximation according to a given…

Statistics Theory · Mathematics 2019-04-03 Alexandra Carpentier , Céline Duval , Ester Mariucci

We will consider the following stochastic differential equation (SDE): \begin{equation} X_t=X_0+\int_0^tb(X_s,\theta_0)ds+\sigma B_t,~~~t\in(0,T], \end{equation} where $\{B_t\}_{t\ge 0}$ is a fractional Brownian motion with Hurst index…

Statistics Theory · Mathematics 2021-12-24 Yasutaka Shimizu , Shohei Nakajima

We study the nonparametric Nadaraya-Watson estimator of the drift function for ergodic stochastic processes driven by fractional Brownian motion of Hurst parameter H > 1/2. The estimator is based on the discretely observed stochastic…

Statistics Theory · Mathematics 2022-05-03 Han Yuecai , Zhang Dingwen

In this paper we study jump-diffusion stochastic differential equations (SDEs) with a discontinuous drift coefficient and a possibly degenerate diffusion coefficient. Such SDEs appear in applications such as optimal control problems in…

Numerical Analysis · Mathematics 2021-01-15 Paweł Przybyłowicz , Michaela Szölgyenyi

Of stochastic differential equations, diffusion processes have been adopted in numerous applications, as more relevant and flexible models. This paper studies diffusion processes in a different setting, where for a given stationary…

Probability · Mathematics 2024-12-31 Saber Jafarizadeh

In this paper, we consider the density estimation problem associated with the stationary measure of ergodic It\^o diffusions from a discrete-time series that approximate the solutions of the stochastic differential equations. To take an…

Numerical Analysis · Mathematics 2021-09-10 Yiqi Gu , John Harlim , Senwei Liang , Haizhao Yang

Timeseries generated from a dynamical source can often be modeled as sample paths of a stochastic differential equation (SDE). The timeseries thus reflects the motion of a particle which flows along the direction provided by a drift /…

Dynamical Systems · Mathematics 2025-11-03 Suddhasattwa Das

We introduce a class of discrete random walk model driven by global memory effects. At any time the right-left transitions depend on the whole previous history of the walker, being defined by an urn-like memory mechanism. The characteristic…

Statistical Mechanics · Physics 2016-12-28 Adrian A. Budini

We consider the problems of parameter estimation for several models of threshold ergodic diffusion processes in the asymptotics of large samples. These models are the direct continuous time analogues of the well-known in time series…

Statistics Theory · Mathematics 2010-03-19 Yury A. Kutoyants

We investigate the value of horizontal turbulent diffusivity {\eta} by numerical calculation of thermal convection. In this study, we introduce a new method whereby the turbulent diffusivity is estimated by monitoring the time devel- opment…

Solar and Stellar Astrophysics · Physics 2015-06-04 H. Hotta , Y. Iida , T. Yokoyama

Convergence of stochastic processes with jumps to diffusion processes is investigated in the case when the limit process has discontinuous coefficients. An example is given in which the diffusion approximation of a queueing model yields a…

Probability · Mathematics 2016-09-07 N. V. Krylov , R. Liptser

This paper uses Lie symmetry methods to calculate certain expectations for a large class of It\^{o} diffusions. We show that if the problem has sufficient symmetry, then the problem of computing functionals of the form $E_x(e^{-\lambda…

Probability · Mathematics 2009-03-02 Mark Craddock , Kelly A. Lennox

We analytically and numerically study a fourth order PDE modeling rough crystal surface diffusion on the macroscopic level. We discuss existence of solutions globally in time and long time dynamics for the PDE model. The PDE, originally…

Analysis of PDEs · Mathematics 2022-11-09 Yuan Gao , Anya E. Katsevich , Jian-Guo Liu , Jianfeng Lu , Jeremy L. Marzuola

Stochastic differential equations are an important modeling class in many disciplines. Consequently, there exist many methods relying on various discretization and numerical integration schemes. In this paper, we propose a novel,…

Machine Learning · Computer Science 2019-05-29 Gabriele Abbati , Philippe Wenk , Michael A Osborne , Andreas Krause , Bernhard Schölkopf , Stefan Bauer
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