spBayes for large univariate and multivariate point-referenced spatio-temporal data models

Andrew O. Finley; Sudipto Banerjee; Alan E. Gelfand

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arXiv · 1310.8192

spBayes for large univariate and multivariate point-referenced spatio-temporal data models

Andrew O. Finley·Sudipto Banerjee·Alan E. Gelfand

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Abstract

In this paper we detail the reformulation and rewrite of core functions in the spBayes R package. These efforts have focused on improving computational efficiency, flexibility, and usability for point-referenced data models. Attention is given to algorithm and computing developments that result in improved sampler convergence rate and efficiency by reducing parameter space; decreased sampler run-time by avoiding expensive matrix computations, and; increased scalability to large datasets by implementing a class of predictive process models that attempt to overcome computational hurdles by representing spatial processes in terms of lower-dimensional realizations. Beyond these general computational improvements for existing model functions, we detail new functions for modeling data indexed in both space and time. These new functions implement a class of dynamic spatio-temporal models for settings where space is viewed as continuous and time is taken as discrete.

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Andrew O. Finley, Sudipto Banerjee, Alan E. Gelfand. 2013-10-30. spBayes for large univariate and multivariate point-referenced spatio-temporal data models. https://arxiv.org/abs/1310.8192

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