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SUMMARY:Accelerating convergence and reducing variance for Langevin sample
 rs
DTSTART:20150617T140000
DTEND:20150617T160000
DTSTAMP:20260406T171933Z
UID:ef82ebd59487d327140bc7d57085f4eab63f0f43c1710cc9b9fae40d
CATEGORIES:Conferences - Seminars
DESCRIPTION:Grigoris Pavliotis\nMarkov Chain Monte Carlo (MCMC) is a stand
 ard methodology for sampling from probability distributions (known up to t
 he normalization constant) in high dimensions.\nThere are (infinitely) man
 y different Markov chains/diffusion processes that can be used to sample f
 rom a given distribution. To reduce the computational complexity\, it is n
 ecessary to consider Markov chains that converge as quickly as possible to
  the  target distribution and that have a small asymptotic variance. In t
 his talk\, I will present some recent results on accelerating convergence 
 to equilibrium and on reducing the asymptotic variance for a class of Lang
 evin-based MCMC algorithms.
LOCATION:BI A0 448 https://plan.epfl.ch/?room==BI%20A0%20448
STATUS:CONFIRMED
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