Parallelizing MCMC for Bayesian spatiotemporal geostatistical models |
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| Authors: | Jun Yan Mary Kathryn Cowles Shaowen Wang Marc P. Armstrong |
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| Affiliation: | (1) Department of Statistics and Actuarial Science, The University of Iowa, Iowa, USA;(2) Department of Biostatistics, The University of Iowa, Iowa, USA;(3) Department of Geography and National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Illinois, USA;(4) Department of Geography, The University of Iowa, Iowa, USA;(5) Program in Applied Mathematical and Computational Science, The University of Iowa, Iowa, USA |
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| 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. |
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| Keywords: | Bayesian inference Markov chain Monte Carlo Parallel computing Spatial modeling |
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