Asymptotics in Wasserstein Distance for Empirical Measures of Markov Processes

Fuente: arXiv
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Autore principale: Wang, Feng-Yu
Natura: Preprint
Pubblicazione: 2024
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author Wang, Feng-Yu
author_facet Wang, Feng-Yu
contents In this paper we introduce some recent progresses on the convergence rate in Wasserstein distance for empirical measures of Markov processes. For diffusion processes on compact manifolds possibly with reflecting or killing boundary conditions, the sharp convergence rate as well as renormalization limits are presented in terms of the dimension of the manifold and the spectrum of the generator. For general ergodic Markov processes, explicit estimates are presented for the convergence rate by using a nice reference diffusion process, which are illustrated by some typical examples. Finally, some techniques are introduced to estimate the Wasserstein distance of empirical measures.
format Preprint
id arxiv_https___arxiv_org_abs_2411_12996
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Asymptotics in Wasserstein Distance for Empirical Measures of Markov Processes
Wang, Feng-Yu
Probability
In this paper we introduce some recent progresses on the convergence rate in Wasserstein distance for empirical measures of Markov processes. For diffusion processes on compact manifolds possibly with reflecting or killing boundary conditions, the sharp convergence rate as well as renormalization limits are presented in terms of the dimension of the manifold and the spectrum of the generator. For general ergodic Markov processes, explicit estimates are presented for the convergence rate by using a nice reference diffusion process, which are illustrated by some typical examples. Finally, some techniques are introduced to estimate the Wasserstein distance of empirical measures.
title Asymptotics in Wasserstein Distance for Empirical Measures of Markov Processes
topic Probability
url https://arxiv.org/abs/2411.12996