Meta-Kriging: Scalable Bayesian Modeling and Inference for Massive Spatial Datasets
Meta-Kriging: Scalable Bayesian Modeling and Inference for Massive Spatial Datasets
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Publisher
United States: Taylor & Francis
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Language
English
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Publisher
United States: Taylor & Francis
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Scope and Contents
Contents
Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations becomes large. There is a burgeoning literature on approaches for analyzing large spatial datasets. In this article, we propose a divide-and-conquer strategy within the Bayesian paradigm. We partition the data into...
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Full title
Meta-Kriging: Scalable Bayesian Modeling and Inference for Massive Spatial Datasets
Authors, Artists and Contributors
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Primary Identifiers
Record Identifier
TN_cdi_jstor_primary_10_2307_45220135
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_jstor_primary_10_2307_45220135
Other Identifiers
ISSN
0040-1706
E-ISSN
1537-2723
DOI
10.1080/00401706.2018.1437474
