article

Parallel algorithms for Markov chain Monte Carlo methods in latent spatial Gaussian models

Authors:

Simon P. WilsonAuthors Info & Claims

Statistics and Computing, Volume 14, Issue 3

Pages 171 - 179

https://doi.org/10.1023/B:STCO.0000035299.51541.5e

Published: 01 August 2004 Publication History

Abstract

Markov chain Monte Carlo (MCMC) implementations of Bayesian inference for latent spatial Gaussian models are very computationally intensive, and restrictions on storage and computation time are limiting their application to large problems. Here we propose various parallel MCMC algorithms for such models. The algorithms' performance is discussed with respect to a simulation study, which demonstrates the increase in speed with which the algorithms explore the posterior distribution as a function of the number of processors. We also discuss how feasible problem size is increased by use of these algorithms.

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Cited By

Tommasel AGodoy DZunino AMateos C(2017)A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selectionKnowledge and Information Systems10.1007/s10115-016-0981-551:2(459-497)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1007/s10115-016-0981-5
Strassburg JAlexandrov V(2013)On scalability behaviour of Monte Carlo sparse approximate inverse for matrix computationsProceedings of the Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems10.1145/2530268.2530274(1-8)Online publication date: 17-Nov-2013
https://dl.acm.org/doi/10.1145/2530268.2530274
Ye JWallace AAl Zain AThompson J(2013)Parallel Bayesian inference of range and reflectance from LaDAR profilesJournal of Parallel and Distributed Computing10.1016/j.jpdc.2012.12.00373:4(383-399)Online publication date: 1-Apr-2013
https://dl.acm.org/doi/10.1016/j.jpdc.2012.12.003
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Parallel algorithms for Markov chain Monte Carlo methods in latent spatial Gaussian models

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cover image Statistics and Computing

Statistics and Computing Volume 14, Issue 3

August 2004

106 pages

ISSN:0960-3174

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Copyright © Copyright © 2004 Kluwer Academic Publishers.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 August 2004

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Cited By

Tommasel AGodoy DZunino AMateos C(2017)A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selectionKnowledge and Information Systems10.1007/s10115-016-0981-551:2(459-497)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1007/s10115-016-0981-5
Strassburg JAlexandrov V(2013)On scalability behaviour of Monte Carlo sparse approximate inverse for matrix computationsProceedings of the Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems10.1145/2530268.2530274(1-8)Online publication date: 17-Nov-2013
https://dl.acm.org/doi/10.1145/2530268.2530274
Ye JWallace AAl Zain AThompson J(2013)Parallel Bayesian inference of range and reflectance from LaDAR profilesJournal of Parallel and Distributed Computing10.1016/j.jpdc.2012.12.00373:4(383-399)Online publication date: 1-Apr-2013
https://dl.acm.org/doi/10.1016/j.jpdc.2012.12.003
Tibbits MHaran MLiechty J(2011)Parallel multivariate slice samplingStatistics and Computing10.1007/s11222-010-9178-z21:3(415-430)Online publication date: 1-Jul-2011
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Steinsland I(2007)Parallel exact sampling and evaluation of Gaussian Markov random fieldsComputational Statistics & Data Analysis10.1016/j.csda.2006.01.01351:6(2969-2981)Online publication date: 1-Mar-2007
https://dl.acm.org/doi/10.1016/j.csda.2006.01.013
Yan JCowles MWang SArmstrong M(2007)Parallelizing MCMC for Bayesian spatiotemporal geostatistical modelsStatistics and Computing10.1007/s11222-007-9022-217:4(323-335)Online publication date: 1-Dec-2007
https://dl.acm.org/doi/10.1007/s11222-007-9022-2

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