Parallelizing MCMC for Bayesian spatiotemporal geostatistical models Export

@article{citeulike:5129952, 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.}, address = {Hingham, MA, USA}, author = {Yan, Jun and Cowles, Mary K. and Wang, Shaowen and Armstrong, Marc P.}, citeulike-article-id = {5129952}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1288825}, citeulike-linkout-1 = {http://dx.doi.org/10.1007/s11222-007-9022-2}, citeulike-linkout-2 = {http://www.springerlink.com/content/w3456lk077637281}, day = {11}, doi = {10.1007/s11222-007-9022-2}, issn = {0960-3174}, journal = {Statistics and Computing}, keywords = {bayesian, gaussian-process, kriging, mcmc, parameterization, spacetime, statistics, sulfur}, month = {December}, number = {4}, pages = {323--335}, posted-at = {2009-07-13 00:53:40}, priority = {2}, publisher = {Kluwer Academic Publishers}, title = {Parallelizing MCMC for Bayesian spatiotemporal geostatistical models}, url = {http://dx.doi.org/10.1007/s11222-007-9022-2}, volume = {17}, year = {2007} }

TY - JOUR ID - citeulike:5129952 L3 - citeulike-article-id:5129952 N2 - 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. IS - 4 JF - Statistics and Computing SN - 0960-3174 AD - Hingham, MA, USA EP - 335 TI - Parallelizing MCMC for Bayesian spatiotemporal geostatistical models PB - Kluwer Academic Publishers VL - 17 SP - 323 KW - bayesian KW - gaussian-process KW - kriging KW - mcmc KW - parameterization KW - spacetime KW - statistics KW - sulfur AU - Yan, Jun AU - Cowles, Mary AU - Wang, Shaowen AU - Armstrong, Marc PY - 2007/12/11/ UR - http://dx.doi.org/10.1007/s11222-007-9022-2 ER -