Statistical Deep Learning for Spatial and Spatiotemporal Data

被引:20
|
作者
Wikle, Christopher K. [1 ]
Zammit-Mangion, Andrew [2 ]
机构
[1] Univ Missouri, Dept Stat, Columbia, MO 65211 USA
[2] Univ Wollongong, Sch Math & Appl Stat, Wollongong, NSW, Australia
基金
澳大利亚研究理事会; 美国国家科学基金会;
关键词
Bayesian hierarchical models; convolutional neural networks; deep Gaussian processes; recurrent neural networks; reinforcement learning; warping; CONVOLUTIONAL NEURAL-NETWORKS; DATA ASSIMILATION; NONSTATIONARY; EMULATOR; MODELS; INFERENCE;
D O I
10.1146/annurev-statistics-033021-112628
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
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.
引用
收藏
页码:247 / 270
页数:24
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