Minwoo Chae (Pohang University of Science and Technology, South Korea) (webinar)
23 October 2020 @ 12:00 - 13:00
“Posterior asymptotics in Wasserstein metrics on the real line”
Joint initiative with MIDAS Complex Data Modeling Research Network https://midas.mat.uc.cl/network/
Abstract: We use the class of Wasserstein metrics to study asymptotic properties of posterior distributions. The first goal is to provide sufficient conditions for posterior consistency. In addition to the well-known Kullback-Leibler condition on the prior, the true distribution and most probability measures in the support of the prior are required to possess moments up to an order which is determined by the order of the Wasserstein metric. We further investigate convergence rates of the posterior distributions for which we need stronger moment conditions. The required tail conditions are sharp in the sense that the posterior distribution may be inconsistent or contract slowly to the true distribution without these conditions. We apply the results to density estimation with a Dirichlet process mixture prior.