Kolyan Ray - Variational Bayes for high-dimensional linear regression with sparse priors
|Starts:||14:00 17 Nov 2021|
|Ends:||15:00 17 Nov 2021|
|What is it:||Seminar|
|Organiser:||Department of Mathematics|
|Who is it for:||University staff, External researchers, Current University students|
Kolyan Ray, Lecturer in Statistics at the Department of Mathematics, Imperial College London is our speaker for the Statistics seminar series.
Title: Variational Bayes for high-dimensional linear regression with sparse priors
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 computational issues and study the empirical performance of the algorithm.
Organisation: Imperial College London
Travel and Contact Information
Zoom link: https://zoom.us/j/92947173491