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Department of Biostatistics

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Practical Bayesian Modeling and Inference for Massive SpatialDatasets On Modest Computing Environments

The data associated with this publication are available upon request.

With continued advances in Geographic Information Systems and related computationaltechnologies, statisticians are often required to analyze very large spatialdatasets. This has generated substantial interest over the last decade, already toovast to be summarized here, in scalable methodologies for analyzing large spatialdatasets. Scalable spatial process models have been found especially attractive dueto their richness and flexibility and, particularly so in the Bayesian paradigm, due totheir presence in hierarchical model settings. However, the vast majority of researcharticles present in this domain have been geared toward innovative theory or morecomplex model development.Very limited attention has been accorded to approachesfor easily implementable scalable hierarchical models for the practicing scientist orspatial analyst. This article devises massively scalable Bayesian approaches that canrapidly deliver inference on spatial process that are practically indistinguishable frominference obtained using more expensive alternatives. A key emphasis is on implementationwithin very standard (modest) computing environments (e.g., a standarddesktop or laptop) using easily available statistical software packages without requiringmessage-parsing interfaces or parallel programming paradigms. Key insights areoffered regarding assumptions and approximations concerning practical efficiency.

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