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Statistical Deep Learning for Spatial and Spatiotemporal Data
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DOI：10.1146/annurev-statistics-033021-112628.png)
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Abstract
En 中文
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.
Keywords:
Bayesian hierarchical models
convolutional neural networks
deep Gaussian processes
recurrent neural networks
reinforcement learning
warping
Journal
IF:
8.7
Papers:
211
Citations:
2.4K
