论文标题
空间统计模型：贝叶斯方法下的概述
Spatial Statistical Models: an overview under the Bayesian Approach
论文作者
论文摘要
考虑到大物联网数据的可用性，设备的小型化和数据存储容量可以实现空间文档的成倍增加。贝叶斯空间统计是通过先验知识和数据可能性确定空间依赖性结构和隐藏模式的有用统计工具。然而，由于它们的简单性，而且通常弱（数据）独立性假设，因此该建模类并未得到很好的探索。通过这种方式，这项系统的审查旨在揭示过去20年中文献中提出的主要模型，并确定差距和研究机会。讨论了诸如随机字段，空间域，事先规范，协方差函数和数值近似值之类的元素。这项工作探讨了空间平滑全球和本地的两个子类。
Spatial documentation is exponentially increasing given the availability of Big IoT Data, enabled by the devices miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns over space through prior knowledge and data likelihood. Nevertheless, this modeling class is not well explored as the classification and regression machine learning models given their simplicity and often weak (data) independence supposition. In this manner, this systematic review aimed to unravel the main models presented in the literature in the past 20 years, identify gaps, and research opportunities. Elements such as random fields, spatial domains, prior specification, covariance function, and numerical approximations were discussed. This work explored the two subclasses of spatial smoothing global and local.
