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This paper establishes an indirect approximation theorem for the most probable transition pathway of a stochastic interacting particle system in the mean-field framework. This paper studied the problem of indirect approximation of the most…

Dynamical Systems · Mathematics 2026-05-27 Jianyu Chen , Ting Gao , Galina Strelkova , Jinqiao Duan

We consider numerical methods for thermodynamic sampling, i.e. computing sequences of points distributed according to the Gibbs-Boltzmann distribution, using Langevin dynamics and overdamped Langevin dynamics (Brownian dynamics). A wide…

Statistical Mechanics · Physics 2015-01-13 Benedict Leimkuhler , Charles Matthews , Gabriel Stoltz

We consider Fokker-Planck type differential operators associated with general Langevin processes admitting a Gibbs stationary distribution. Under assumptions insuring suitable resolvent estimates, we prove Eyring-Kramers formulas for the…

Analysis of PDEs · Mathematics 2022-01-06 Jean-Francois Bony , Dorian Le Peutrec , Laurent Michel

Wigner's jellium is a model for a gas of electrons. The model consists of $N$ unit negatively charged particles lying in a sea of neutralizing homogeneous positive charge spread out according to Lebesgue measure, and interactions are…

Probability · Mathematics 2022-07-20 Christian Hirsch , Sabine Jansen , Paul Jung

We propose and analyze a class of adaptive sampling algorithms for multimodal distributions on a bounded domain, which share a structural resemblance to the classic overdamped Langevin dynamics. We first demonstrate that this class of…

Machine Learning · Computer Science 2024-11-26 Björn Engquist , Kui Ren , Yunan Yang

This work is devoted to deriving the Onsager-Machlup action functional for a class of stochastic differential equations with (non-Gaussian) L\'{e}vy process as well as Brownian motion in high dimensions. This is achieved by applying the…

Dynamical Systems · Mathematics 2024-06-19 Jianyu Hu , Jianyu Chen

The statistical analysis for equations driven by fractional Gaussian process (fGp) is relatively recent. The development of stochastic calculus with respect to the fGp allowed to study such models. In the present paper we consider the drift…

Probability · Mathematics 2016-09-28 Mohamed El Machkouri , Khalifa Es-Sebaiy , Youssef Ouknine

Modern applications of Bayesian inference involve models that are sufficiently complex that the corresponding posterior distributions are intractable and must be approximated. The most common approximation is based on Markov chain Monte…

Machine Learning · Statistics 2019-05-15 Yue Yang , Ryan Martin , Howard Bondell

In this paper we compute and analyse the transition rates and duration of reactive trajectories of the stochastic 1-D Allen-Cahn equations for both the Freidlin-Wentzell regime (weak noise or temperature limit) and finite-amplitude white…

Fluid Dynamics · Physics 2015-12-04 Joran Rolland , Freddy Bouchet , Eric Simonnet

This paper proposes a new family of lower and upper bounds on the minimum mean squared error (MMSE). The key idea is to minimize/maximize the MMSE subject to the constraint that the joint distribution of the input-output statistics lies in…

Information Theory · Computer Science 2020-06-09 Michael Fauß , Alex Dysto , H. Vincent Poor

We study normal diffusive and subdiffusive processes in a harmonic potential (Ornstein-Uhlenbeck process) on a uniformly growing/contracting domain. Our starting point is a recently derived fractional Fokker-Planck equation, which covers…

Statistical Mechanics · Physics 2019-07-31 F. Le Vot , S. B. Yuste , E. Abad

Ornstein-Uhlenbeck process of bounded variation is introduced as a solution of an analogue of the Langevin equation with an integrated telegraph process replacing a Brownian motion. There is an interval $I$ such that the process starting…

Probability · Mathematics 2020-07-17 Nikita Ratanov

The main result of this article regards a small time approximation for the Girsanov's exponential. We prove that the latter is well described over short time intervals by the solution of a deterministic partial differential equation.The…

Probability · Mathematics 2021-11-29 Ramiro Scorolli

We provide guarantees for approximate Gaussian Process (GP) regression resulting from two common low-rank kernel approximations: based on random Fourier features, and based on truncating the kernel's Mercer expansion. In particular, we…

Machine Learning · Statistics 2022-02-22 Constantinos Daskalakis , Petros Dellaportas , Aristeidis Panos

We provide guarantees for approximate Gaussian Process (GP) regression resulting from two common low-rank kernel approximations: based on random Fourier features, and based on truncating the kernel's Mercer expansion. In particular, we…

Machine Learning · Statistics 2021-12-16 Constantinos Daskalakis , Petros Dellaportas , Aristeidis Panos

We consider the adaptive test for the parameter change in discretely observed ergodic diffusion processes based on the cusum test. Using two test statistics based on the two quasi-log likelihood functions of the diffusion parameter and the…

Statistics Theory · Mathematics 2020-04-30 Yozo Tonaki , Yusuke Kaino , Masayuki Uchida

A simple and very flexible variational approach to the out-of-equilibrium quantum dynamics in strongly correlated electron systems is introduced through a time-dependent Gutzwiller wavefunction. As an application, we study the simple case…

Strongly Correlated Electrons · Physics 2010-09-10 Marco Schiró , Michele Fabrizio

We obtain solutions to conservation laws under any random initial conditions that are described by Gaussian stochastic processes (in some cases discretized). We analyze the generalization of Burgers' equation for a smooth flux function…

Analysis of PDEs · Mathematics 2018-05-14 Carey Caginalp

We consider the Curie-Weiss model at a given initial temperature in vanishing external field evolving under a Glauber spin-flip dynamics corresponding to a possibly different temperature. We study the limiting conditional probabilities and…

Mathematical Physics · Physics 2015-05-18 Victor Ermolaev , Christof Kuelske

We revisit the work of Mitter and Newton on an information-theoretic interpretation of Bayes' formula through the Gibbs variational principle. This formulation allowed them to pose nonlinear estimation for diffusion processes as a problem…

Optimization and Control · Mathematics 2024-05-02 Maxim Raginsky