Orateur
Jordan Serres
Description
Stein’s method is a collection of tools designed to quantify the closeness between two probability measures. It was initially developed extensively to measure the distance to the Gaussian distribution and has since been generalized to measures with full support on $\mathbb{R}^d$. The application of this method to uniform distributions over different domains presents additional difficulties due to boundary effects, and has only recently been explored, allowing the introduction of new notions of distance between domains.
In this talk, I will present the fundamental ideas of Stein's method for shapes, and then mention an application of the method to derive stability estimates in Steklov's spectral problem.
