Authors : Robert, Christian P (Author of the conference)
CIRM (Publisher )
Abstract :
In this talk, we derive a novel non-reversible, continuous-time Markov chain Monte Carlo (MCMC) sampler, called Coordinate Sampler, based on a piecewise deterministic Markov process (PDMP), which can be seen as a variant of the Zigzag sampler. In addition to proving a theoretical validation for this new sampling algorithm, we show that the Markov chain it induces exhibits geometrical ergodicity convergence, for distributions whose tails decay at least as fast as an exponential distribution and at most as fast as a Gaussian distribution. Several numerical examples highlight that our coordinate sampler is more efficient than the Zigzag sampler, in terms of effective sample size.
[This is joint work with Wu Changye, ref. arXiv:1809.03388]
Keywords : MCMC; no-reversibility; Gibbs sampling
MSC Codes :
60J25
- Continuous-time Markov processes on general state spaces
62F15
- Bayesian inference
Film maker : Hennenfent, Guillaume
Language : English
Available date : 02/11/2020
Conference Date : 02/11/2020
Subseries : Research School
arXiv category : Statistics Theory
Mathematical Area(s) : Probability & Statistics
Format : MP4 (.mp4) - HD
Video Time : 00:32:44
Targeted Audience : Researchers
Download : https://videos.cirm-math.fr/2020-11-05_Robert.mp4
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Event Title : Jean-Morlet Chair 2020 - Research School: Quasi-Monte Carlo Methods and Applications / Chaire Jean-Morlet 2020 - Ecole: Méthode de quasi-Monte-Carlo et applications Event Organizers : Rivat, Joël ; Thonhauser, Stefan ; Tichy, Robert Dates : 02/11/2020 - 07/11/2020
Event Year : 2020
Event URL : https://www.chairejeanmorlet.com/2255.html
DOI : 10.24350/CIRM.V.19664703
Cite this video as:
Robert, Christian P (2020). The coordinate sampler: a non-reversible Gibbs-like MCMC sampler. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19664703
URI : http://dx.doi.org/10.24350/CIRM.V.19664703
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Bibliography