Stein’s Method in Computational Statistics
December 8, @ Errol St.

Abstract: Stein’s method is a powerful technique from probability theory that can be used to bound the distance between probability measures. Although the method was initially designed as a technique for proving central limit theorems, it has recently caught the attention of the computational statistics and machine learning communities, with applications that include generative modelling, variational inference, sampling, control variates, and measuring the quality of Markov chain Monte Carlo algorithms.

This workshop will introduce the tools needed for Stein’s method, and then discuss several recent breakthroughs in computational statistics that have resulted from Stein’s method.

Call for Posters: There will be a poster session over lunch for the physical participants – if you are interested in presenting a poster, please contact .

In-person registration:
Online registration: