Efficient algorithms for Bayesian Nearest Neighbor Gaussian Processes

TL;DR

This paper introduces improved algorithms for Bayesian Nearest Neighbor Gaussian Processes that enhance computational efficiency, convergence, and robustness, demonstrated through simulated and real LiDAR data for forest canopy mapping.

Contribution

The paper proposes new formulations of NNGP models that optimize memory management and leverage high-performance libraries for faster, more reliable Bayesian inference.

Findings

Enhanced convergence and speed in NNGP models

Robust Bayesian inference with improved algorithms

First statistically robust forest canopy map for TIU

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.