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Paperback. Condition: Very Good. We are unable to ship to Canada at this time.Ex-library paperback in very nice condition with the usual markings and attachments. Text block clean and unmarked. Tight binding.
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Condition: As New. Unread book in perfect condition.
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Theoretical Computer Science: Exploring New Frontiers of Theoretical Informatics; Intermational Conference IFIP TCS 2000 Sendai, Japan, August 17-19, 2000 Proceedings
Published by Springer Verlag, New York, 2000
ISBN 10: 3540678239 ISBN 13: 9783540678236
Language: English
Hardcover. Condition: Very Good. No Jacket. Hardcover 2000 library bound edition. Ex-library book with stamps and labels attached. Binding firm. Pages unmarked and clean. Covers and text in very good condition. Series: Lecture Notes in Computer Science ;1872. [xv, 630 p. : ill. ; 24 cm].
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Data Assimilation Fundamentals
Published by Springer Nature Switzerland AG, CH, 2023
ISBN 10: 3030967115 ISBN 13: 9783030967116
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: New. 2022 ed. This open-access textbook's significant contribution is the unified derivation of data-assimilation techniques from a common fundamental and optimal starting point, namely Bayes' theorem. Unique for this book is the "top-down" derivation of the assimilation methods. It starts from Bayes theorem and gradually introduces the assumptions and approximations needed to arrive at today's popular data-assimilation methods. This strategy is the opposite of most textbooks and reviews on data assimilation that typically take a bottom-up approach to derive a particular assimilation method. E.g., the derivation of the Kalman Filter from control theory and the derivation of the ensemble Kalman Filter as a low-rank approximation of the standard Kalman Filter. The bottom-up approach derives the assimilation methods from different mathematical principles, making it difficult to compare them. Thus, it is unclear which assumptions are made to derive an assimilation method and sometimes even which problem it aspires to solve. The book's top-down approach allows categorizing data-assimilation methods based on the approximations used. This approach enables the user to choose the most suitable method for a particular problem or application. Have you ever wondered about the difference between the ensemble 4DVar and the "ensemble randomized likelihood" (EnRML) methods? Do you know the differences between the ensemble smoother and the ensemble-Kalman smoother? Would you like to understand how a particle flow is related to a particle filter? In this book, we will provide clear answers to several such questions. The book provides the basis for an advanced course in data assimilation. It focuses on the unified derivation of the methods and illustrates their properties on multiple examples. It is suitable for graduate students, post-docs, scientists, and practitioners working in data assimilation.
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Data Assimilation Fundamentals: A Unified Formulation of the State and Parameter Estimation Problem (Springer Textbooks in Earth Sciences, Geography and Environment)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
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Data Assimilation Fundamentals : A Unified Formulation of the State and Parameter Estimation Problem
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Data Assimilation Fundamentals : A Unified Formulation of the State and Parameter Estimation Problem
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
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Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
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Condition: New. SUPER FAST SHIPPING.
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Condition: New.
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Condition: New.
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Condition: New. pp. 116.
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Condition: As New. Unread book in perfect condition.
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The Challenges of Water Management and Governance in Cities
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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The Challenges of Water Management and Governance in Cities
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Paperback. Condition: Brand New. 264 pages. 9.25x6.10x0.56 inches. In Stock.
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The Challenges of Water Management and Governance in Cities
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Paperback. Condition: Brand New. 118 pages. 8.90x6.10x0.40 inches. In Stock.
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Data Assimilation Fundamentals
Published by Springer Nature Switzerland AG, CH, 2023
ISBN 10: 3030967115 ISBN 13: 9783030967116
Language: English
Paperback. Condition: New. 2022 ed. This open-access textbook's significant contribution is the unified derivation of data-assimilation techniques from a common fundamental and optimal starting point, namely Bayes' theorem. Unique for this book is the "top-down" derivation of the assimilation methods. It starts from Bayes theorem and gradually introduces the assumptions and approximations needed to arrive at today's popular data-assimilation methods. This strategy is the opposite of most textbooks and reviews on data assimilation that typically take a bottom-up approach to derive a particular assimilation method. E.g., the derivation of the Kalman Filter from control theory and the derivation of the ensemble Kalman Filter as a low-rank approximation of the standard Kalman Filter. The bottom-up approach derives the assimilation methods from different mathematical principles, making it difficult to compare them. Thus, it is unclear which assumptions are made to derive an assimilation method and sometimes even which problem it aspires to solve. The book's top-down approach allows categorizing data-assimilation methods based on the approximations used. This approach enables the user to choose the most suitable method for a particular problem or application. Have you ever wondered about the difference between the ensemble 4DVar and the "ensemble randomized likelihood" (EnRML) methods? Do you know the differences between the ensemble smoother and the ensemble-Kalman smoother? Would you like to understand how a particle flow is related to a particle filter? In this book, we will provide clear answers to several such questions. The book provides the basis for an advanced course in data assimilation. It focuses on the unified derivation of the methods and illustrates their properties on multiple examples. It is suitable for graduate students, post-docs, scientists, and practitioners working in data assimilation.
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Condition: New. SUPER FAST SHIPPING.
