Dey Debangan, Datta Abhirup, Banerjee Sudipto
Department of Biostatistics, Johns Hopkins University, USA.
Department of Biostatistics, University of California Los Angeles, USA.
N Engl J Stat Data Sci. 2023 Sep;1(2):283-295. doi: 10.51387/23-nejsds47. Epub 2023 Sep 6.
Graphical models have witnessed significant growth and usage in spatial data science for modeling data referenced over a massive number of spatial-temporal coordinates. Much of this literature has focused on a single or relatively few spatially dependent outcomes. Recent attention has focused upon addressing modeling and inference for substantially large number of outcomes. While spatial factor models and multivariate basis expansions occupy a prominent place in this domain, this article elucidates a recent approach, graphical Gaussian Processes, that exploits the notion of conditional independence among a very large number of spatial processes to build scalable graphical models for fully model-based Bayesian analysis of multivariate spatial data.
图形模型在空间数据科学中已得到显著发展和广泛应用，用于对大量时空坐标上的数据进行建模。该领域的许多文献都聚焦于单个或相对较少的空间相关结果。最近的关注重点则是解决大量结果的建模和推断问题。虽然空间因子模型和多元基展开在这一领域占据显著地位，但本文阐述了一种新方法——图形高斯过程，它利用大量空间过程之间的条件独立性概念，来构建可扩展的图形模型，用于对多元空间数据进行基于完全模型的贝叶斯分析。
