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We consider alternate formulations of recently proposed hierarchical NearestNeighbor Gaussian Process (NNGP) models (Datta et al., 2016a) for improvedconvergence, faster computing time, and more robust and reproducible Bayesianinference. Algorithms are defined that improve CPU memory management andexploit existing high-performance numerical linear algebra libraries.Computational and inferential benefits are assessed for alternate NNGPspecifications using simulated datasets and remotely sensed light detection andranging (LiDAR) data collected over the US Forest Service Tanana Inventory Unit(TIU) in a remote portion of Interior Alaska. The resulting data product is thefirst statistically robust map of forest canopy for the TIU.

Efficient algorithms for Bayesian Nearest Neighbor Gaussian Processes