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.
Original language | English (US) |
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Pages (from-to) | 401-414 |
Number of pages | 14 |
Journal | Journal of Computational and Graphical Statistics |
Volume | 28 |
Issue number | 2 |
DOIs | |
State | Published - Apr 3 2019 |
Keywords
- 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