skip to content

Monte Carlo without rejection

Presented by: 
Alexandre Bouchard
Monday 3rd July 2017 - 14:15 to 15:00
INI Seminar Room 1
Co-authors: Arnaud Doucet (Oxford), Sebastian Vollmer (Warwick), George Deligiannidis (King's College London), Paul Vanetti (Oxford)

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 transition kernels built up over the Metropolis-Hastings algorithm. In our recent work, we investigate an alternative approach, the Bouncy Particle Sampler (BPS) where the target distribution of interest is explored using a continuous-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 modified by bouncing against this barrier. The resulting non-reversible Markov process provides a rejection-free Markov chain Monte Carlo sampling scheme. This method, inspired from recent work in the molecular simulation literature, is shown to be a valid, efficient sampling scheme applicable to a wide range of Bayesian problems. We present several additional original 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.
The video for this talk should appear here if JavaScript is enabled.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons