计算机科学
统计模型
人工智能
深度学习
机器学习
人工神经网络
高斯过程
光学(聚焦)
数据建模
空间分析
数据科学
贝叶斯概率
数据挖掘
高斯分布
地理
物理
遥感
量子力学
数据库
光学
作者
Christopher K. Wikle,Andrew Zammit‐Mangion
标识
DOI:10.1146/annurev-statistics-033021-112628
摘要
Deep neural network models have become ubiquitous in recent years and have been applied to nearly all areas of science, engineering, and industry. These models are particularly useful for data that have strong dependencies in space (e.g., images) and time (e.g., sequences). Indeed, deep models have also been extensively used by the statistical community to model spatial and spatiotemporal data through, for example, the use of multilevel Bayesian hierarchical models and deep Gaussian processes. In this review, we first present an overview of traditional statistical and machine learning perspectives for modeling spatial and spatiotemporal data, and then focus on a variety of hybrid models that have recently been developed for latent process, data, and parameter specifications. These hybrid models integrate statistical modeling ideas with deep neural network models in order to take advantage of the strengths of each modeling paradigm. We conclude by giving an overview of computational technologies that have proven useful for these hybrid models, and with a brief discussion on future research directions.
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