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Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data.

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Title: Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data.
Authors: McDermott PL; Jupiter Intelligence, Boulder, CO 80302, USA., Wikle CK; Department of Statistics, University of Missouri, Columbia, MO 65211, USA.
Source: Entropy (Basel, Switzerland) [Entropy (Basel)] 2019 Feb 15; Vol. 21 (2). Date of Electronic Publication: 2019 Feb 15.
Publication Type: Journal Article
Journal Info: Publisher: MDPI Country of Publication: Switzerland NLM ID: 101243874 Publication Model: Electronic Cited Medium: Internet ISSN: 1099-4300 (Electronic) Linking ISSN: 10994300 NLM ISO Abbreviation: Entropy (Basel) Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
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        Value: 10.3390/e21020184
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      – TitleFull: Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data.
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              Text: 2019 Feb 15
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