Reliability statistical computing modeling (5 Ergebnisse)
Sprache: Englisch
Verlag: Springer, 2021
Serie: Buch 67 von 90 - Springer Series in Reliability Engineering
- Softcover
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Zustand: New. In.
Sprache: Englisch
Verlag: Springer Nature, 2021
Serie: Buch 67 von 90 - Springer Series in Reliability Engineering
- Softcover
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Paperback. Zustand: Brand New. 330 pages. 9.25x6.10x0.75 inches. In Stock.
Sprache: Englisch
Verlag: Springer, 2021
Serie: Buch 67 von 90 - Springer Series in Reliability Engineering
- Softcover
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Taschenbuch. Zustand: Neu. Reliability and Statistical Computing | Modeling, Methods and Applications | Hoang Pham | Taschenbuch | Springer Series in Reliability Engineering | xiii | Englisch | 2021 | Springer | EAN 9783030434144 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juer…gen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer International Publishing, Springer International Publishing, 2021
Serie: Buch 67 von 90 - Springer Series in Reliability Engineering
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents the latest developments in both qualitative and quantitative computational methods for reliability and statistics, as well as their applications. Consisting of contributions from active researchers and experienced practitioners in…the field, it fills the gap between theory and practice and explores new research challenges in reliability and statistical computing. The book consists of 18 chapters. It covers (1) modeling in and methods for reliability computing, with chapters dedicated to predicted reliability modeling, optimal maintenance models, and mechanical reliability and safety analysis; (2) statistical computing methods, including machine learning techniques and deep learning approaches for sentiment analysis and recommendation systems; and (3) applications and case studies, such as modeling innovation paths of European firms, aircraft components, bus safety analysis, performance prediction in textile finishing processes, and movie recommendation systems.Given its scope, the book will appeal to postgraduates, researchers, professors, scientists, and practitioners in a range of fields, including reliability engineering and management, maintenance engineering, quality management, statistics, computer science and engineering, mechanical engineering, business analytics, and data science.
Sprache: Englisch
Verlag: Springer International Publishing, 2020
Serie: Buch 67 von 90 - Springer Series in Reliability Engineering
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents the latest developments in both qualitative and quantitative computational methods for reliability and statistics, as well as their applications. Consisting of contributions from active researchers and experienced practitioners in the fie…ld, it fills the gap between theory and practice and explores new research challenges in reliability and statistical computing. The book consists of 18 chapters. It covers (1) modeling in and methods for reliability computing, with chapters dedicated to predicted reliability modeling, optimal maintenance models, and mechanical reliability and safety analysis; (2) statistical computing methods, including machine learning techniques and deep learning approaches for sentiment analysis and recommendation systems; and (3) applications and case studies, such as modeling innovation paths of European firms, aircraft components, bus safety analysis, performance prediction in textile finishing processes, and movie recommendation systems.Given its scope, the book will appeal to postgraduates, researchers, professors, scientists, and practitioners in a range of fields, including reliability engineering and management, maintenance engineering, quality management, statistics, computer science and engineering, mechanical engineering, business analytics, and data science.
