— Lecture 12 —

Probabilistic Inference and Learning with Stein's Method

Lecturer: Lester Mackey (Microsoft Research)
Date:
Time: (Zurich time)
Slides: Click here to download!
Recording: Click here to view! (only for ETH members)

Abstract:

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

Recommended reading:

  • Anastasiou, A. et al. (2021): Stein’s Method Meets Statistics: A Review of Some Recent Developments. arXiv:2105.03481. [Sections 1, 2, 4, and 5]
  • Gorham, J. and Mackey, L. (2017): Measuring Sample Quality with Kernels. arXiv:1703.01717. [optional]
  • Gorham, J. and Mackey, L. (2015): Measuring Sample Quality with Stein’s Method. arXiv:1506.03039. [optional]