Speaker：Jian Wang（Fujian Normal University）

Time：2022-04-22, 15:00

Location：Tencent Meeting ID：878916887（No Password）

Abstract:

We study contractions of Markov chains on general metric spaces with respect to distance-like functions, which are comparable to the total variation and the standard $L^p$-Wasserstein distances for $p \ge 1$. By employing the refined basic coupling and the coupling by reflection, the results are applied to Markov chains whose transitions include additive stochastic noises that are not necessarily isotropic.  Motivated by recent works on the use of heavy tailed processes in Markov Chain Monte Carlo, we show that chains driven by the $\alpha$-stable noise can have better contraction rates than corresponding chains driven by the Gaussian noise.