Abstract

Stein's method is a powerful tool from probability theory for bounding the distance between probability distributions. In this talk, I'll describe how this tool designed to prove central limit theorems can be adapted to assess and improve the quality of practical inference procedures. I'll highlight applications to Markov chain Monte Carlo sampler selection, goodness-of-fit testing, variational inference, and nonconvex optimization and close with several opportunities for future work.

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