Holdings: On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory

On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory:

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Bibliographic Details
Main Author:	Guignard, Fabian (Author)
Format:	Electronic eBook
Language:	English
Published:	Cham Springer International Publishing 2022 Cham Springer
Edition:	1st ed. 2022
Series:	Springer Theses, Recognizing Outstanding Ph.D. Research
Subjects:	Earth and Environmental Sciences Computer Modelling Renewable Energy Earth sciences Geography Computer simulation Renewable energy sources
Online Access:	BTU01 FAN01 FAW01 FFW01 FHD01 FHM01 TUM01 UBA01 UBG01 UBM01 UBT01 UEI01 UPA01 Volltext
Physical Description:	1 Online-Ressource (XVIII, 158 p. 68 illus., 43 illus. in color)
ISBN:	9783030952310
ISSN:	2190-5061
DOI:	10.1007/978-3-030-95231-0

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MARC


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Record in the Search Index

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series2	Springer Theses, Recognizing Outstanding Ph.D. Research
spelling	Guignard, Fabian Verfasser aut On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory by Fabian Guignard 1st ed. 2022 Cham Springer International Publishing 2022 Cham Springer 1 Online-Ressource (XVIII, 158 p. 68 illus., 43 illus. in color) txt rdacontent c rdamedia cr rdacarrier Springer Theses, Recognizing Outstanding Ph.D. Research 2190-5061 Earth and Environmental Sciences Computer Modelling Renewable Energy Earth sciences Geography Computer simulation Renewable energy sources Erscheint auch als Druck-Ausgabe 978-3-030-95230-3 Erscheint auch als Druck-Ausgabe 978-3-030-95232-7 Erscheint auch als Druck-Ausgabe 978-3-030-95233-4 https://doi.org/10.1007/978-3-030-95231-0 Verlag URL des Erstveröffentlichers Volltext
spellingShingle	Guignard, Fabian On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory Earth and Environmental Sciences Computer Modelling Renewable Energy Earth sciences Geography Computer simulation Renewable energy sources
title	On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory
title_auth	On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory
title_exact_search	On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory
title_exact_search_txtP	On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory
title_full	On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory by Fabian Guignard
title_fullStr	On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory by Fabian Guignard
title_full_unstemmed	On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory by Fabian Guignard
title_short	On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory
title_sort	on spatio temporal data modelling and uncertainty quantification using machine learning and information theory
topic	Earth and Environmental Sciences Computer Modelling Renewable Energy Earth sciences Geography Computer simulation Renewable energy sources
topic_facet	Earth and Environmental Sciences Computer Modelling Renewable Energy Earth sciences Geography Computer simulation Renewable energy sources
url	https://doi.org/10.1007/978-3-030-95231-0
work_keys_str_mv	AT guignardfabian onspatiotemporaldatamodellinganduncertaintyquantificationusingmachinelearningandinformationtheory

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