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]