Abstract
Markov chain Monte Carlo (MCMC) implementations of Bayesian inference for latent spatial Gaussian models are very computationally intensive, and restrictions on storage and computation time are limiting their application to large problems. Here we propose three parallel algorithms for linear algebra calculations in these implementations. The algorithms’ performance is discussed with respect to a simulation study, which demonstrates the increase in speed and feasible problem size as a function of the number of processors. We discuss how parallel algorithms may be useful more generally in MCMC schemes for these models.
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Whiley, M. and Wilson, S.P. (2001). A note on using parallel algorithms for implementing Bayesian inference in spatial Gaussian models. Technical Report 01/01, Department of Statistics, Trinity College Dublin.
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© 2002 Springer-Verlag Berlin Heidelberg
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Whiley, M., Wilson, S.P. (2002). Parallel Algorithms for Bayesian Inference in Spatial Gaussian Models. In: Härdle, W., Rönz, B. (eds) Compstat. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57489-4_74
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DOI: https://doi.org/10.1007/978-3-642-57489-4_74
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1517-7
Online ISBN: 978-3-642-57489-4
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