Empirical approximation to invariant measures for McKean-Vlasov processes
Professor Dai KU
Date & Time
30 Nov 2022 (Wed) | 10:00 AM - 11:00 AM
Venue
Online Zoom
ABSTRACT
This work obtains that, under a monotonicity condition, the invariant probability measure of a McKean-Vlasov process can be approximated by weighted empirical measures of some processes including itself. These processes are described by distribution dependent or empirical measure dependent stochastic differential equations constructed from the equation for the McKean-Vlasov process. Convergence of empirical measures is characterized by upper bound estimates for their Wasserstein distance to the invariant measure. The theoretical results are demonstrated via a mean-field Ornstein-Uhlenbeck process.