Details:

Alternative Title:
Wiley Interdiscip Rev Comput Stat
Personal Author:
Datta, Abhirup ; Banerjee, Sudipto ; Finley, Andrew O. ; Gelfand, Alan E.
Datta, Abhirup ; Banerjee, Sudipto ; Finley, Andrew O. ; Gelfand, Alan E. Less -
Description:
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, Gelfand AE. Hierarchical nearest-neighbor gaussian process models for large geostatistical datasets. | 2016., JASA) provide a scalable alternative by using local information from few nearest neighbors. Scalability is achieved by using the neighbor sets in a conditional specification of the model. We show how this is equivalent to sparse modeling of Cholesky factors of large covariance matrices. We also discuss a general approach to construct scalable Gaussian Processes using sparse local kriging. We present a multivariate data analysis which demonstrates how the nearest neighbor approach yields inference indistinguishable from the full rank GP despite being several times faster. Finally, we also propose a variant of the NNGP model for automating the selection of the neighbor set size.
Subjects:
[+]
Article Bayesian Methods And Theory Computational Bayesian Methods Data Structures Image And Spatial Data
Source:
Wiley Interdiscip Rev Comput Stat. 8(5):162-171.
Pubmed ID:
29657666
Pubmed Central ID:
PMC5894878
Document Type:
Journal Article
Funding:
R01 OH010093/OH/NIOSH CDC HHS/United States ; RC1 GM092400/GM/NIGMS NIH HHS/United States
R01 OH010093/OH/NIOSH CDC HHS/United States ; RC1 GM092400/GM/NIGMS NIH HHS/United States Less -
Collection(s):
CDC Public Access
Main Document Checksum:
[+]
urn:sha256:e52355f22e9522552c0e1ffa3739496b341fcdd89bdead16565417f0ddc1ef3c
Download URL:
https://stacks.cdc.gov/view/cdc/53590/cdc_53590_DS1.pdf
File Type:

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