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Verlag: Pergamon Press, 1965
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Sprache: Englisch
Verlag: SIAM Society for Industrial and Applied Mathematics, 2022
ISBN 10: 1611977126 ISBN 13: 9781611977127
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Sprache: Englisch
Verlag: Society For Industrial & Applied Mathematics,U.S., 2022
ISBN 10: 1611977126 ISBN 13: 9781611977127
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Taschenbuch. Zustand: Neu. Statistical Methods for Materials Science | The Data Science of Microstructure Characterization | Jeffrey P. Simmons (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2021 | CRC Press | EAN 9780367780289 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
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Sprache: Englisch
Verlag: Taylor & Francis Inc, Bosa Roca, 2019
ISBN 10: 1498738206 ISBN 13: 9781498738200
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Hardcover. Zustand: new. Hardcover. Data analytics has become an integral part of materials science. This book provides the practical tools and fundamentals needed for researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to microstructure characterization. It contains valuable guidance on essential topics such as denoising and data modeling. Additionally, the analysis and applications section addresses compressed sensing methods, stochastic models, extreme estimation, and approaches to pattern detection. This book provides the practical tools and fundamentals needed for researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to microstructure characterization. It contains valuable guidance on essential topics such as denoising and data modeling. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Sprache: Englisch
Verlag: Taylor & Francis Inc, Bosa Roca, 2019
ISBN 10: 1498738206 ISBN 13: 9781498738200
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Hardcover. Zustand: new. Hardcover. Data analytics has become an integral part of materials science. This book provides the practical tools and fundamentals needed for researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to microstructure characterization. It contains valuable guidance on essential topics such as denoising and data modeling. Additionally, the analysis and applications section addresses compressed sensing methods, stochastic models, extreme estimation, and approaches to pattern detection. This book provides the practical tools and fundamentals needed for researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to microstructure characterization. It contains valuable guidance on essential topics such as denoising and data modeling. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data analytics has become an integral part of materials science. This book provides the practical tools and fundamentals needed for researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to microstructure characterization. It contains valuable guidance on essential topics such as denoising and data modeling. Additionally, the analysis and applications section addresses compressed sensing methods, stochastic models, extreme estimation, and approaches to pattern detection. 538 pp. Englisch.
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In den WarenkorbZustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Jeffrey P. Simmons, Lawrence F. Drummy, Charles A. Bouman, Marc De GraefData analytics has become an integral part of materials science. This book provides the practical tools and fundamentals needed for researchers in materials science to understand.
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Data analytics has become an integral part of materials science. This book provides the practical tools and fundamentals needed for researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to microstructure characterization. It contains valuable guidance on essential topics such as denoising and data modeling. Additionally, the analysis and applications section addresses compressed sensing methods, stochastic models, extreme estimation, and approaches to pattern detection.