This short paper is centered on hierarchical modeling for problems in spatial and spatio-temporal statistics. It draws its motivation from the interdisciplinary research work of the author in terms of applications in the environmental sciences—ecological processes, environmental exposure, and weather modeling. The paper briefly reviews hierarchical modeling specification, adopting a Bayesian perspective with full inference and associated uncertainty within the specification, while achieving exact inference to avoid what may be uncomfortable asymptotics. It focuses on point-referenced (geo-statistical) and point pattern spatial settings. It looks in some detail at problems involving data fusion, species distributions, and large spatial datasets. It also briefly describes four further examples arising from the author’s recent research projects.

这篇简短的论文以空间和时空统计问题的分层建模为中心。它的动机来自作者在环境科学（生态过程、环境暴露和天气建模）中的应用方面的跨学科研究工作。该论文简要回顾了分层建模规范，采用贝叶斯视角，在规范内具有完整的推理和相关的不确定性，同时实现精确推理以避免可能不舒服的渐近。它侧重于点参考（地理统计）和点模式空间设置。它详细研究了涉及数据融合、物种分布和大型空间数据集的问题。它还简要描述了作者最近的研究项目中产生的四个进一步的例子。