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SUMMARY:The Bouncy Particle Sampler - Alexandre Bouchard-Côté (UBC)
DTSTART:20170505T150000Z
DTEND:20170505T160000Z
UID:TALK71958@talks.cam.ac.uk
CONTACT:Quentin Berthet
DESCRIPTION:Markov chain Monte Carlo methods have become standard tools to
  sample from complex high-dimensional probability measures. Many available
  techniques rely on discrete-time reversible Markov chains whose transitio
 n kernels built up over the Metropolis-Hastings algorithm. In our recent w
 ork\, we investigate an alternative approach\, the Bouncy Particle Sampler
  (BPS) where the target distribution of interest is explored using a conti
 nuous-time\, non reversible Markov process. In this alternative approach\,
  a particle moves along straight lines continuously around the space and\,
  when facing a high energy barrier\, it is not rejected but its path is mo
 dified by bouncing against this barrier. The resulting non-reversible Mark
 ov process provides a rejection-free Markov chain Monte Carlo sampling sch
 eme. This method\, inspired from recent work in the molecular simulation l
 iterature\, is shown to be a valid\, efficient sampling scheme applicable 
 to a wide range of Bayesian problems. We present several additional origin
 al methodological extensions and establish various theoretical properties 
 of these procedures. We demonstrate experimentally the efficiency of these
  algorithms on a variety of Bayesian inference problems.
LOCATION:MR12\, Centre for Mathematical Sciences\, Wilberforce Road\, Camb
 ridge.
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