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Published by Springer, 2023
ISBN 10: 3030967115ISBN 13: 9783030967116
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Published by Springer, 2023
ISBN 10: 3030967115ISBN 13: 9783030967116
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ISBN 10: 3030967115ISBN 13: 9783030967116
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Published by Springer, 2006
ISBN 10: 354038300XISBN 13: 9783540383000
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ISBN 10: 3030967115ISBN 13: 9783030967116
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ISBN 10: 3030967115ISBN 13: 9783030967116
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ISBN 10: 3030967115ISBN 13: 9783030967116
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Paperback or Softback. Condition: New. Data Assimilation Fundamentals: A Unified Formulation of the State and Parameter Estimation Problem 0.84. Book.
Published by Springer, 2023
ISBN 10: 3030967115ISBN 13: 9783030967116
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Published by Springer International Publishing Apr 2023, 2023
ISBN 10: 3030967115ISBN 13: 9783030967116
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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'stop-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. 268 pp. Englisch.
Published by Springer, 2006
ISBN 10: 354038300XISBN 13: 9783540383000
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Published by Springer, 2023
ISBN 10: 3030967115ISBN 13: 9783030967116
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Published by Springer Nature, 2023
ISBN 10: 3030967115ISBN 13: 9783030967116
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Published by Springer International Publishing Apr 2022, 2022
ISBN 10: 3030967085ISBN 13: 9783030967086
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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'stop-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. 268 pp. Englisch.
Published by Springer International Publishing Apr 2022, 2022
ISBN 10: 3030967085ISBN 13: 9783030967086
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Book Print on Demand
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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'stop-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. 268 pp. Englisch.
Published by Springer International Publishing, 2023
ISBN 10: 3030967115ISBN 13: 9783030967116
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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'stop-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.
Published by Springer International Publishing, 2023
ISBN 10: 3030967115ISBN 13: 9783030967116
Seller: moluna, Greven, Germany
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Derives data-assimilation methods using a top-down approachPresents unified data-assimilation formulation Derivation applicable to both state- and parameter estimationProvides a deep understanding of data-assimilation methods and the.
Published by Springer International Publishing, 2022
ISBN 10: 3030967085ISBN 13: 9783030967086
Seller: AHA-BUCH GmbH, Einbeck, Germany
Book
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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'stop-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.
Published by Springer, 2022
ISBN 10: 3030967085ISBN 13: 9783030967086
Seller: GF Books, Inc., Hawthorne, CA, U.S.A.
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ISBN 10: 3030967085ISBN 13: 9783030967086
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Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Derives data-assimilation methods using a top-down approachPresents unified data-assimilation formulation Derivation applicable to both state- and parameter estimationProvides a deep understanding of data-assimilation methods and the.
Published by Springer, 2014
ISBN 10: 3642424767ISBN 13: 9783642424762
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ISBN 10: 3642037100ISBN 13: 9783642037108
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ISBN 10: 3642424767ISBN 13: 9783642424762
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ISBN 10: 3642424767ISBN 13: 9783642424762
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ISBN 10: 3642424767ISBN 13: 9783642424762
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Published by Springer, 2014
ISBN 10: 3642424767ISBN 13: 9783642424762
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Published by Springer, 2009
ISBN 10: 3642037100ISBN 13: 9783642037108
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ISBN 10: 3642037100ISBN 13: 9783642037108
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ISBN 10: 3642037100ISBN 13: 9783642037108
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