论文标题
具有空间变化的协方差内核的非平稳空间过程模型
Nonstationary Spatial Process Models with Spatially Varying Covariance Kernels
论文作者
论文摘要
在提供计算高效推理的同时构建捕获非平稳行为的空间过程模型是具有挑战性的。非平稳的空间变化内核（参见，例如Paciorek，2003）具有灵活性和丰富性，但是计算受到空间变化过程参数而产生的高维参数空间的阻碍。如果记录测量值的位置数量大规模，则事情会加剧。在理论上的障碍有限的情况下，消除计算瓶颈需要模型构建和算法开发之间的协同作用。我们使用空间变化的协方差内核构建一类可扩展的非平稳空间过程模型。我们使用与嵌套交织的混合蒙特卡洛实施贝叶斯建模框架。我们对综合数据集进行实验，以探索模型选择和参数可识别性，并评估非组织建模的推论改进。我们用遥感归一化差异植被指数的数据集说明了优势和陷阱。
Building spatial process models that capture nonstationary behavior while delivering computationally efficient inference is challenging. Nonstationary spatially varying kernels (see, e.g., Paciorek, 2003) offer flexibility and richness, but computation is impeded by high-dimensional parameter spaces resulting from spatially varying process parameters. Matters are exacerbated if the number of locations recording measurements is massive. With limited theoretical tractability, obviating computational bottlenecks requires synergy between model construction and algorithm development. We build a class of scalable nonstationary spatial process models using spatially varying covariance kernels. We implement a Bayesian modeling framework using Hybrid Monte Carlo with nested interweaving. We conduct experiments on synthetic data sets to explore model selection and parameter identifiability, and assess inferential improvements accrued from nonstationary modeling. We illustrate strengths and pitfalls with a data set on remote sensed normalized difference vegetation index.
