Parallelizing MCMC for Bayesian spatiotemporal geostatistical models

Jun Yan, Mary Kathryn Cowles, Shaowen Wang, Marc P. Armstrong

Research output: Contribution to journalArticlepeer-review

Abstract

When MCMC methods for Bayesian spatiotemporal modeling are applied to large geostatistical problems, challenges arise as a consequence of memory requirements, computing costs, and convergence monitoring. This article describes the parallelization of a reparametrized and marginalized posterior sampling (RAMPS) algorithm, which is carefully designed to generate posterior samples efficiently. The algorithm is implemented using the Parallel Linear Algebra Package (PLAPACK). The scalability of the algorithm is investigated via simulation experiments that are implemented using a cluster with 25 processors. The usefulness of the method is illustrated with an application to sulfur dioxide concentration data from the Air Quality System database of the U.S. Environmental Protection Agency.

Original languageEnglish (US)
Pages (from-to)323-335
Number of pages13
JournalStatistics and Computing
Volume17
Issue number4
DOIs
StatePublished - Dec 2007

Keywords

  • Bayesian inference
  • Markov chain Monte Carlo
  • Parallel computing
  • Spatial modeling

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics

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