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.

中文翻译：

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贝叶斯最近邻高斯过程的高效算法

摘要：我们考虑最近提出的分层最近邻高斯过程（NNGP）模型的替代公式，以改进收敛性、更快的计算时间以及更稳健和可再现的贝叶斯推理。定义的算法可改进 CPU 内存管理并利用现有的高性能数值线性代数库。使用模拟数据集和通过美国林务局塔纳纳库存单位 (TIU) 在阿拉斯加内陆偏远地区收集的遥感光探测和测距数据，评估替代 NNGP 规范的计算和推理效益。由此产生的数据产品是 TIU 第一份统计上可靠的森林冠层地图。本文的补充材料可在线获取。