Kingston Greer (5 results)
Published by Vintage, New York, 2013
- Softcover
Seller: Blue Moon Books, Stevens Point, WI, U.S.A.Blue Moon Books
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Add to basketTrade Paperback. Condition: Very Good. Very Good++++. Bright and attractive trade paperback. Light wear ot corners. Very nice copy.
- Softcover
Seller: preigu, Osnabrück, Germanypreigu
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Taschenbuch. Condition: Neu. Bayesian Artificial Neural Networks | with Applications in Water Resources Engineering | Greer Kingston (u. a.) | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639223248 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…
Bayesian Artificial Neural Networks: With Applications In Water Resources Engineering
Kingston, Greer; Maier, Holger; Lambert, Martin; Kingston, Greer; Maier, Holger; Lambert, Martin
- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Paperback. Condition: Brand New. 372 pages. 8.58x5.83x0.94 inches. In Stock.
- Softcover
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Seller: moluna, Greven, Germanymoluna
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kingston GreerGreer Kingston gained her PhD from the University of Adelaide, Australia. She has since continued to work in the field of statistical modelling with particular focus on natural catastrophe risk. Holger Maier and Martin .…
- Softcover
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Given the nonlinearity, complexity and limited physical understanding of many processes occurring within water resource systems, artificial neural networks may at times be the best available tool for modelling such systems. However, despite the increasing use of neural network models, they are still viewed with some scepticism by users of more conventional modelling methodologies, primarily due to their black box nature. In this book, a new Bayesian framework for developing artificial neural networks is presented, which aims to address what are considered to be the three most significant issues hindering the wider acceptance of artificial neural networks in the field of water resources engineering; namely generalisability, interpretability and uncertainty. Throughout the development of this framework, emphasis is placed on obtaining accurate results, while maintaining simplicity of implementation, which is considered to be of utmost importance for adoption of the framework by practitioners in the field of water resources engineering.…
