Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes

Andrew O. Finley, Abhirup Datta, Bruce D. Cook, Douglas C. Morton, Hans E. Andersen, Sudipto Banerjee

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)401-414
Number of pages14
JournalJournal of Computational and Graphical Statistics
Issue number2
StatePublished - Apr 3 2019


  • Bayesian methods
  • Computationally intensive methods
  • Spatial analysis
  • Statistical computing
  • Stochastic processes

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Discrete Mathematics and Combinatorics


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