  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.
