Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes. Issue 2 (3rd April 2019)

• Record Type: Journal Article
• Authors: Finley, Andrew O.; Datta, Abhirup; Cook, Bruce D.; Morton, Douglas C.; Andersen, Hans E.; Banerjee, Sudipto
• Is Part Of: Journal of computational and graphical statistics. Volume 28:Issue 2(2019)
• Page Start: 401
• Page End: 414
• Publication Date: 2019-04-03
• Subjects: Bayesian methods -- Computationally intensive methods -- Spatial analysis -- Statistical computing -- Stochastic processes; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2018.1537924
• Languages: English
• ISSNs: 1061-8600

Abstract

ABSTRACT: 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.