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DTSTAMP:20250716T055233Z
DESCRIPTION:Title: Variational Bayes for high-dimensional linear regression
  with sparse priors.\n\n\n	Abstract:\n\n\nA core problem in Bayesian statis
 tics is approximating difficult to compute posterior distributions. In var
 iational Bayes (VB)\, a method from machine learning\, one approximates th
 e posterior through optimization\, which is typically faster than Markov c
 hain Monte Carlo. We study a mean-field (i.e. factorizable) VB approximati
 on to Bayesian model selection priors\, including the popular spike-and-sl
 ab prior\, in sparse high-dimensional linear regression. We establish conv
 ergence rates for this VB approach\, studying conditions under which it pr
 ovides good estimation. We also discuss some computational issues and stud
 y the empirical performance of the algorithm.\n\n\n	Speaker\n\n\nKolyan Ray
  is a Lecturer in Statistics at the Department of Mathematics at Imperial 
 College London. His research interests include Bayesian nonparametrics\, c
 ausal inference\, variational inference\, inverse problems and asymptotic 
 statistics. Prior to this\, he was a Lecturer in Statistics at King’s Coll
 ege London\, a Postdoc at Leiden University under Aad van der Vaart\, and 
 obtained his PhD at the University of Cambridge under Richard Nickl.\n\nht
 tps://mcgill.zoom.us/j/83436686293?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09\n
 \nMeeting ID: 834 3668 6293\n\nPasscode: 12345\n
DTSTART:20211112T203000Z
DTEND:20211112T213000Z
SUMMARY:Kolyan Ray (Imperial College London
URL:/mathstat/channels/event/kolyan-ray-imperial-colle
 ge-london-334679
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