Monte Carlo without rejection
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About this item
| Description: |
Bouchard, A
Monday 3rd July 2017 - 14:15 to 15:00 |
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| Created: | 2017-07-21 10:03 |
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| Collection: | Scalable inference; statistical, algorithmic, computational aspects |
| Publisher: | Isaac Newton Institute |
| Copyright: | Bouchard, A |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | 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. |
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