主講人：杜愷 復(fù)旦大學(xué)研究員

時(shí)間：2022年11月10日14：00

地點(diǎn)：騰訊會(huì)議 203 959 918

舉辦單位：數(shù)理學(xué)院

內(nèi)容介紹：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.