Wasserstein Convergence Rate for Empirical Measures of Markov Processes

Abstract

The convergence rate in Wasserstein distance is estimated for empirical measures of ergodic Markov processes, and the estimate can be sharp in some specific situations. The main result is applied to subordinations of typical models excluded by existing results, which include: stochastic Hamiltonian systems on $\mathbb R^{n}\times \mathbb R^{m}$, spherical velocity Langevin processes on $\mathbb R^n\times\mathbb S^{n-1},$ multi-dimensional Wright-Fisher type diffusion processes, and stable type jump processes.