Piecewise deterministic Markov processes and efficiency gains through exact subsampling for MCMC
50 mins 23 secs,
92.17 MB,
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Description: |
Bierkens, J
Tuesday 18th July 2017 - 10:20 to 11:00 |
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Created: | 2017-07-19 11:27 |
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Collection: | Scalable inference; statistical, algorithmic, computational aspects |
Publisher: | Isaac Newton Institute |
Copyright: | Bierkens, J |
Language: | eng (English) |
Distribution: | World (downloadable) |
Explicit content: | No |
Aspect Ratio: | 16:9 |
Screencast: | No |
Bumper: | UCS Default |
Trailer: | UCS Default |
Abstract: | 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 Markov chains whose transition kernel is obtained via the Metropolis- Hastings algorithm, there has been recent interest in alternative schemes based on piecewise deterministic Markov processes (PDMPs). One such approach is 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 theoretical results. |
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