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. |
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| 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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