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Efficient algorithms for Bayesian Nearest Neighbor Gaussian Processes.

ABSTRACT: We consider alternate formulations of recently proposed hierarchical Nearest Neighbor Gaussian Process (NNGP) models (Datta et al., 2016a) 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 (LiDAR) data collected over the US 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.

SUBMITTER: Finley AO

PROVIDER: S-EPMC6753955 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Efficient algorithms for Bayesian Nearest Neighbor Gaussian Processes.

Finley Andrew O AO Datta Abhirup A Cook Bruce C BC Morton Douglas C DC Andersen Hans E HE Banerjee Sudipto S

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 20190401 2

We consider alternate formulations of recently proposed hierarchical Nearest Neighbor Gaussian Process (NNGP) models (Datta et al., 2016a) 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 sen ...[more]

PMID: 31543693

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