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Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets
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 geos...
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| I publikationen: | J Am Stat Assoc |
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| Huvudupphovsmän: | , , , |
| Materialtyp: | Artigo |
| Språk: | Inglês |
| Publicerad: |
2016
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| Ämnen: | |
| Länkar: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5927603/ https://ncbi.nlm.nih.gov/pubmed/29720777 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1080/01621459.2015.1044091 |
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