Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets
Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical 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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Contents
Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations become large. This article develops a class of highly scalable nearest-neighbor Gaussian process (NNGP) models to provide fully model-based inference for large geostatistical datasets. We establish that the NNGP is...
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Full title
Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets
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Record Identifier
TN_cdi_crossref_primary_10_1080_01621459_2015_1044091
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_crossref_primary_10_1080_01621459_2015_1044091
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ISSN
0162-1459,1537-274X
E-ISSN
1537-274X
DOI
10.1080/01621459.2015.1044091
