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 language | English (US) |
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Pages (from-to) | 323-335 |
Number of pages | 13 |
Journal | Statistics and Computing |
Volume | 17 |
Issue number | 4 |
DOIs | |
State | Published - 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