Comparison of deep neural networks and deep hierarchical models for spatio-temporal data

DOI10.1007/s13253-019-00361-7zbMath1426.62362arXiv1902.08321OpenAlexW2926657291WikidataQ128155842 ScholiaQ128155842MaRDI QIDQ2419837

Christopher K. Wikle

Publication date: 4 June 2019

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1902.08321

zbMATH Keywords

recurrent neural networkdynamic modelBayesianecho state networkconvolutional neural networkESNCNNRNN

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15) Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics (62-01) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

Bayesian modeling of discrete-time point-referenced spatio-temporal data ⋮ Measuring, mapping, and uncertainty quantification in the space-time cube ⋮ Computer Model Calibration with Time Series Data Using Deep Learning and Quantile Regression

Uses Software

• BayesDA
• AlphaZero
• VBayesLab

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