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Robert, Christian P., Elvira, Victor, Tawn, Nicholas and Wu, Changye (2018) Accelerating MCMC algorithms. Wiley Interdisciplinary Reviews: Computational Statistics, 10 (5). e1435. doi:10.1002/wics.1435
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Official URL: https://doi.org/10.1002/wics.1435
Abstract
Markov chain Monte Carlo algorithms are used to simulate from complex statistical distributions by way of a local exploration of these distributions. This local feature avoids heavy requests on understanding the nature of the target, but it also potentially induces a lengthy exploration of this target, with a requirement on the number of simulations that grows with the dimension of the problem and with the complexity of the data behind it. Several techniques are available towards accelerating the convergence of these Monte Carlo algorithms, either at the exploration level (as in tempering, Hamiltonian Monte Carlo and partly deterministic methods) or at the exploitation level (with Rao-Blackwellisation and scalable methods).
| Item Type: | Journal Article | ||||||||
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| Subjects: | H Social Sciences > HA Statistics Q Science > QA Mathematics |
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| Divisions: | Faculty of Science > Statistics | ||||||||
| Library of Congress Subject Headings (LCSH): | Markov processes, Monte Carlo method, Algorithms, Statistics | ||||||||
| Journal or Publication Title: | Wiley Interdisciplinary Reviews: Computational Statistics | ||||||||
| Publisher: | John Wiley & Sons, Inc. | ||||||||
| ISSN: | 0006-3444 | ||||||||
| Official Date: | September 2018 | ||||||||
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| Volume: | 10 | ||||||||
| Number: | 5 | ||||||||
| Article Number: | e1435 | ||||||||
| DOI: | 10.1002/wics.1435 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access | ||||||||
| RIOXX Funder/Project Grant: |
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