Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes

We consider alternate formulations of recently proposed hierarchical nearest neighbor Gaussian process (NNGP) models for improved convergence, faster computing time, and more robust and reproducible Bayesian inference. Algorithms are defined that improve CPU memory management and exploit existing hi...

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Bibliographic Details
Published in:Journal of computational and graphical statistics 2019-04, Vol.28 (2), p.401-414
Main Authors: Finley, Andrew O., Datta, Abhirup, Cook, Bruce D., Morton, Douglas C., Andersen, Hans E., Banerjee, Sudipto
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Bayesian methods
Computationally intensive methods
Computer simulation
Computing time
Forests
Formulations
Gaussian process
Lidar
Linear algebra
Memory management
Remote sensing
Robustness (mathematics)
Spatial analysis
Statistical computing
Statistical inference
Stochastic processes
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Summary:We consider alternate formulations of recently proposed hierarchical nearest neighbor Gaussian process (NNGP) models for improved convergence, faster computing time, and more robust and reproducible Bayesian inference. Algorithms are defined that improve CPU memory management and exploit existing high-performance numerical linear algebra libraries. Computational and inferential benefits are assessed for alternate NNGP specifications using simulated datasets and remotely sensed light detection and ranging data collected over the U.S. Forest Service Tanana Inventory Unit (TIU) in a remote portion of Interior Alaska. The resulting data product is the first statistically robust map of forest canopy for the TIU. Supplemental materials for this article are available online.
ISSN:1061-8600
1537-2715
DOI:10.1080/10618600.2018.1537924