Statistical Deep Learning for Spatial and Spatiotemporal Data

Deep neural network models have become ubiquitous in recent years and been applied to nearly all areas of science, engineering, industry. These are particularly useful for data that strong dependencies space (e.g., images) time sequences). Indeed, deep also extensively used by the statistical community model spatial spatiotemporal through, example, use multilevel Bayesian hierarchical Gaussian processes. In this review, we first present an overview traditional machine learning perspectives modeling data, then focus on a variety hybrid recently developed latent process, parameter specifications. integrate ideas with order take advantage strengths each paradigm. We conclude giving computational technologies proven these models, brief discussion future research directions.