Geographic Information Systems (GIS) and related technologies have generated substantial interest among statisticians with regard to scalable methodologies for analyzing large spatial datasets. A variety of scalable spatial process models have been proposed that can be easily embedded within a hierarchical modeling framework to carry out Bayesian inference. While the focus of statistical research has mostly been directed toward innovative and more complex model development, relatively limited attention has been accorded to approaches for easily implementable scalable hierarchical models for the practicing scientist or spatial analyst. This article discusses how point-referenced spatial process models can be cast as a conjugate Bayesian linear regression that can rapidly deliver inference on spatial processes. The approach allows exact sampling directly (avoids iterative algorithms such as Markov chain Monte Carlo) from the joint posterior distribution of regression parameters, the latent process and the predictive random variables, and can be easily implemented on statistical programming environments such as R.

地理信息系统 (GIS) 和相关技术已引起统计学家对用于分析大型空间数据集的可扩展方法的极大兴趣。已经提出了各种可扩展的空间过程模型，这些模型可以很容易地嵌入到分层建模框架中以进行贝叶斯推理。虽然统计研究的重点主要集中在创新和更复杂的模型开发上，但对于为执业科学家或空间分析师提供易于实施的可扩展分层模型的方法，关注度相对有限。本文讨论了如何将点参考空间过程模型转换为可以快速提供空间过程推断的共轭贝叶斯线性回归。R。 _