Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes
改进了分层最近邻高斯过程模型，提升收敛速度和计算效率，并用模拟数据和阿拉斯加森林冠层LiDAR数据验证了其稳健的贝叶斯推断能力。
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.