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On nearest-neighbor Gaussian process models for massive spatial data
Gaussian Process (GP) models provide a very flexible nonparametric approach to modeling location-and-time indexed datasets. However, the storage and computational requirements for GP models are infeasible for large spatial datasets. Nearest Neighbor Gaussian Processes (Datta A, Banerjee S, Finley AO...
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| Pubblicato in: | Wiley Interdiscip Rev Comput Stat |
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| Autori principali: | , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
2016
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5894878/ https://ncbi.nlm.nih.gov/pubmed/29657666 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/wics.1383 |
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