Date: 2021-11-12

Time: 15:30-16:30 (Montreal time)

https://mcgill.zoom.us/j/83436686293?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09

Meeting ID: 834 3668 6293

Passcode: 12345

Abstract:

A core problem in Bayesian statistics is approximating difficult to compute posterior distributions. In variational Bayes (VB), a method from machine learning, one approximates the posterior through optimization, which is typically faster than Markov chain Monte Carlo. We study a mean-field (i.e. factorizable) VB approximation to Bayesian model selection priors, including the popular spike-and-slab prior, in sparse high-dimensional linear regression. We establish convergence rates for this VB approach, studying conditions under which it provides good estimation. We also discuss some computational issues and study the empirical performance of the algorithm.

Speaker

Kolyan Ray is a Lecturer in Statistics at the Department of Mathematics at Imperial College London. His research interests include Bayesian nonparametrics, causal inference, variational inference, inverse problems and asymptotic statistics. Prior to this, he was a Lecturer in Statistics at King’s College London, a Postdoc at Leiden University under Aad van der Vaart, and obtained his PhD at the University of Cambridge under Richard Nickl.