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Accelerating MCMC algorithms

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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 ISSN 0006-3444.

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Official URL: https://doi.org/10.1002/wics.1435

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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
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics
Divisions: Faculty of Science, Engineering and Medicine > 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
Dates:
DateEvent
September 2018Published
13 June 2018Available
25 April 2018Accepted
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 (Creative Commons)
Date of first compliant deposit: 26 April 2018
Date of first compliant Open Access: 25 September 2018
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
EP/K014463/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266

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