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Numerical Linear Algebra for Applications in Statistics (Statistics and Computing)
Published by Springer (edition 1998), 1998
ISBN 10: 0387985425 ISBN 13: 9780387985428
Language: English
Hardcover. Condition: Very Good. 1998. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.
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hardcover. Condition: Good. Clean text,.
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hardcover. Condition: As New. Unmarked hardcover.
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US$ 59.42
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Paperback or Softback. Condition: New. Numerical Linear Algebra for Applications in Statistics 0.75. Book.
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Hardback or Cased Book. Condition: New. Numerical Linear Algebra for Applications in Statistics 1.16. Book.
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Add to basketCondition: Gut. Zustand: Gut - Gebrauchs- und Lagerspuren. Innen: Kleiner Riss. | Seiten: 240 | Sprache: Englisch | Produktart: Bücher.
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Numerical Linear Algebra for Applications in Statistics
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Numerical Linear Algebra for Applications in Statistics (Statistics and Computing)
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Numerical Linear Algebra for Applications in Statistics (Statistics and Computing)
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Numerical Linear Algebra For Applications In Statistics
Published by Springer Nature, 2020
Language: English
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Numerical Linear Algebra for Applications in Statistics
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Numerical linear algebra is one of the most important subjects in the field of statistical computing. Statistical methods in many areas of application require computations with vectors and matrices. This book describes accurate and efficient computer algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors. Although the book is not tied to any particular software system, it describes and gives examples of the use of modern computer software for numerical linear algebra. An understanding of numerical linear algebra requires basic knowledge both of linear algebra and of how numerical data are stored and manipulated in the computer. The book begins with a discussion of the basics of numerical computations, and then describes the relevant properties of matrix inverses, matrix factorizations, matrix and vector norms, and other topics in linear algebra; hence, the book is essentially self- contained. The topics addressed in this bookconstitute the most important material for an introductory course in statistical computing, and should be covered in every such course. The book includes exercises and can be used as a text for a first course in statistical computing or as supplementary text for various courses that emphasize computations. James Gentle is University Professor of Computational Statistics at George Mason University. During a thirteen-year hiatus from academic work before joining George Mason, he was director of research and design at the world's largest independent producer of Fortran and C general-purpose scientific software libraries. These libraries implement many algorithms for numerical linear algebra. He is a Fellow of the American Statistical Association and member of the International Statistical Institute. He has held several national.
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Numerical Linear Algebra for Applications in Statistics
Published by Springer New York, Springer New York, 1998
ISBN 10: 0387985425 ISBN 13: 9780387985428
Language: English
US$ 67.48
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Add to basketBuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Numerical linear algebra is one of the most important subjects in the field of statistical computing. Statistical methods in many areas of application require computations with vectors and matrices. This book describes accurate and efficient computer algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors. Although the book is not tied to any particular software system, it describes and gives examples of the use of modern computer software for numerical linear algebra. An understanding of numerical linear algebra requires basic knowledge both of linear algebra and of how numerical data are stored and manipulated in the computer. The book begins with a discussion of the basics of numerical computations, and then describes the relevant properties of matrix inverses, matrix factorizations, matrix and vector norms, and other topics in linear algebra; hence, the book is essentially self- contained. The topics addressed in this bookconstitute the most important material for an introductory course in statistical computing, and should be covered in every such course. The book includes exercises and can be used as a text for a first course in statistical computing or as supplementary text for various courses that emphasize computations. James Gentle is University Professor of Computational Statistics at George Mason University. During a thirteen-year hiatus from academic work before joining George Mason, he was director of research and design at the world's largest independent producer of Fortran and C general-purpose scientific software libraries. These libraries implement many algorithms for numerical linear algebra. He is a Fellow of the American Statistical Association and member of the International Statistical Institute. He has held several national.
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Numerical Linear Algebra for Applications in Statistics
Published by Springer New York Okt 2012, 2012
ISBN 10: 1461268427 ISBN 13: 9781461268420
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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US$ 61.64
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Accurate and efficient computer algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors. Regardless of the software system used, the book describes and gives examples of the use of modern computer software for numerical linear algebra. It begins with a discussion of the basics of numerical computations, and then describes the relevant properties of matrix inverses, factorisations, matrix and vector norms, and other topics in linear algebra. The book is essentially self- contained, with the topics addressed constituting the essential material for an introductory course in statistical computing. Numerous exercises allow the text to be used for a first course in statistical computing or as supplementary text for various courses that emphasise computations. 240 pp. Englisch.
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Numerical Linear Algebra for Applications in Statistics
Published by Springer New York Aug 1998, 1998
ISBN 10: 0387985425 ISBN 13: 9780387985428
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
US$ 61.64
Convert currencyUS$ 25.73 shipping from Germany to U.S.A.Quantity: 2 available
Add to basketBuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Numerical linear algebra is one of the most important subjects in the field of statistical computing. Statistical methods in many areas of application require computations with vectors and matrices. This book describes accurate and efficient computer algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors. Although the book is not tied to any particular software system, it describes and gives examples of the use of modern computer software for numerical linear algebra. An understanding of numerical linear algebra requires basic knowledge both of linear algebra and of how numerical data are stored and manipulated in the computer. The book begins with a discussion of the basics of numerical computations, and then describes the relevant properties of matrix inverses, matrix factorizations, matrix and vector norms, and other topics in linear algebra; hence, the book is essentially self- contained. The topics addressed in this book constitute the most important material for an introductory course in statistical computing, and should be covered in every such course. The book includes exercises and can be used as a text for a first course in statistical computing or as supplementary text for various courses that emphasize computations. James Gentle is University Professor of Computational Statistics at George Mason University. During a thirteen-year hiatus from academic work before joining George Mason, he was director of research and design at the world's largest independent producer of Fortran and C general-purpose scientific software libraries. These libraries implement many algorithms for numerical linear algebra. 240 pp. Englisch.
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Numerical Linear Algebra for Applications in Statistics
Published by Springer-Verlag New York Inc., 2012
ISBN 10: 1461268427 ISBN 13: 9781461268420
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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US$ 76.86
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Add to basketPaperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 370.
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Numerical Linear Algebra for Applications in Statistics
Published by Springer-Verlag New York Inc., 1998
ISBN 10: 0387985425 ISBN 13: 9780387985428
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
US$ 76.86
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Add to basketHardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 544.
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US$ 54.43
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Accurate and efficient computer algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors. Regardless of the software system used, the book describes and gives examples of the use of modern computer .
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US$ 54.43
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Add to basketGebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Accurate and efficient computer algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors. Regardless of the software system used, the book describes and gives examples of the use of modern computer .
