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SUMMARY:Piecewise deterministic Markov processes and efficiency gains thro
 ugh exact subsampling for MCMC - Joris Bierkens (Delft University of Techn
 ology)
DTSTART:20170718T092000Z
DTEND:20170718T100000Z
UID:TALK73961@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Markov chain Monte Carlo methods provide an essential tool in 
 statistics for sampling from complex probability distributions. While the 
 standard approach to MCMC involves constructing discrete-time reversible M
 arkov chains whose transition kernel is obtained via the Metropolis- Hasti
 ngs algorithm\, there has been recent interest in alternative schemes base
 d on piecewise deterministic Markov processes (PDMPs). One such approach i
 s based on the Zig-Zag process\, introduced in Bierkens and Roberts (2016)
 \, which proved to provide a highly scalable sampling scheme for sampling 
 in the big data regime (Bierkens\, Fearnhead and Roberts (2016)). In this 
 talk we will present a broad overview of these methods along with some the
 oretical results.  <br><br><br><br>
LOCATION:Seminar Room 1\, Newton Institute
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