Parallelizing MCMC for Bayesian spatiotemporal geostatistical models

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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Correspondence to Jun Yan.

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Yan, J., Cowles, M.K., Wang, S. et al. Parallelizing MCMC for Bayesian spatiotemporal geostatistical models. Stat Comput 17, 323–335 (2007). https://doi.org/10.1007/s11222-007-9022-2

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Keywords

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