Stein’s method for multivariate continuous distributions and applications

Abstract:

Stein’s method is a key method for assessing distributional distance, mainly for one-dimensional distributions. In this talk we provide a general approach to Stein’s method for multivariate continuous distributions. Among the applications we consider is the Wasserstein distance between two continuous probability distributions under the assumption of existence of a Poincare constant.

This is joint work with Guillaume Mijoule (INRIA Paris) and Yvik Swan (Liege).

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Bio: Gesine Reinert is a Research Professor of the Department of Statistics and of Keble College, both University of Oxford. She is also a Fellow of the Alan Turing Institute and a Fellow of the IMS. Her research is centered around Stein’s method and analysis of networks and other complex structures. She currently chairs the Applied Probability Section of the Royal Statistical Society. She is the vice-chair of the European Cooperation for Statistics of Network Data Science.