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Publication Detail

Title: Efficient algorithms for Bayesian Nearest Neighbor Gaussian Processes.

Authors: Finley, Andrew O; Datta, Abhirup; Cook, Bruce C; Morton, Douglas C; Andersen, Hans E; Banerjee, Sudipto

Published In J Comput Graph Stat, (2019)

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.

PubMed ID: 31543693 Exiting the NIEHS site

MeSH Terms: No MeSH terms associated with this publication

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