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SUMMARY:Bayesian Inferences and the Laplace-Bernstein-von Mises Theorem  -
  Professor Richard Nickl
DTSTART:20231020T170000Z
DTEND:20231020T180000Z
UID:TALK207604@talks.cam.ac.uk
CONTACT:106940
DESCRIPTION:Bayesian inference has been widely used in the statistical sci
 ences and applied mathematics - systematically so since Laplace's 'Theorie
  analytique' from 1812. It has seen a 'dark age' of subjectivist thinking 
 in the 20th century due to computational infeasibility\, but then emerged\
 , with the advent of modern Markov chain Monte Carlo methods in the 1990s\
 , as a popular paradigm for data driven inference under uncertainty. Nowad
 ays Bayesian algorithms are among the most commonly used methods in statis
 tics and machine learning. We will discuss some mathematical theorems that
  explain when one can trust such algorithms in the high-dimensional contex
 t of modern data science.\n\n
LOCATION:MR2 in the CMS
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