This book explains how computer software is designed to perform the tasks required for sophisticated statistical analysis. For statisticians, it examines the nitty-gritty computational problems behind statistical methods. For mathematicians and computer scientists, it looks at the application of mathematical tools to statistical problems. The first half of the book offers a basic background in numerical analysis that emphasizes issues important to statisticians. The next several chapters cover a broad array of statistical tools, such as maximum likelihood and nonlinear regression. The author also treats the application of numerical tools; numerical integration and random number generation are explained in a unified manner reflecting complementary views of Monte Carlo methods. Each chapter contains exercises that range from simple questions to research problems. Most of the examples are accompanied by demonstration and source code available in from the author's Web site. New in this second edition are demonstrations coded in R, as well as new sections on linear programming and the Nelder-Mead search algorithm.

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This book explains how computer software is designed to perform the tasks required for sophisticated statistical analysis. The first half of the book provides a basic background in numerical analysis emphasizing issues important to statisticians. The next several chapters cover a broad array of applications, such as maximum likelihood and nonlinear regression, numerical integration and random number generation, Monte Carlo methods, sorting, FFT and the application of other "fast" algorithms to statistics. Numerous examples are accompanied by demonstration code available on a floppy disk included with the book. This graduate text will also be a valuable reference for statisticians, mathematicians, and numerical analysts.

This book explains how computer software is designed to perform the tasks required for sophisticated statistical analysis. Each chapter contains exercises that range from simple questions to research problems. Most of the examples are accompanied by demonstration and source code available in from the author's Web site. New in this second edition are demonstrations coded in R, as well as new sections on linear programming and the Nelder-Mead search algorithm.

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ISBN 10: 1107665930
ISBN 13: 9781107665934

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**Book Description **Cambridge University Press, 2011. Softcover. Book Condition: New. 2nd edition. 18 x 24 cm. This book explains how computer software is designed to perform the tasks required for sophisticated statistical analysis. For statisticians, it examines the nitty-gritty computational problems behind statistical methods. For mathematicians and computer scientists, it looks at the application of mathematical tools to statistical problems. The first half of the book offers a basic background in numerical analysis that emphasizes issues important to statisticians. The next several chapters cover a broad array of statistical tools, such as maximum likelihood and nonlinear regression. The author also treats the application of numerical tools; numerical integration and random number generation are explained in a unified manner reflecting complementary views of Monte Carlo methods. Each chapter contains exercises that range from simple questions to research problems. Most of the examples are accompanied by demonstration and source code available from the author`s website. New in this second edition are demonstrations coded in R, as well as new sections on linear programming and the Nelder?Mead search algorithm. Contents 1. Algorithms and computers 2. Computer arithmetic 3. Matrices and linear equations 4. More methods for solving linear equations 5. Least squares 6. Eigenproblems 7. Functions: interpolation, smoothing and approximation 8. Introduction to optimization and nonlinear equations 9. Maximum likelihood and nonlinear regression 10. Numerical integration and Monte Carlo methods 11. Generating random variables from other distributions 12. Statistical methods for integration and Monte Carlo 13. Markov chain Monte Carlo methods 14. Sorting and fast algorithms. Printed Pages: 458. Bookseller Inventory # 39475

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ISBN 10: 1107665930
ISBN 13: 9781107665934

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**Book Description **Book Condition: New. Brand New Paperback International Edition.We Ship to PO BOX Address also. EXPEDITED shipping option also available for faster delivery. Bookseller Inventory # AUSBNEW-43894

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Published by
Cambridge University Press
(2011)

ISBN 10: 1107665930
ISBN 13: 9781107665934

New
Softcover
Quantity Available: > 20

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**Book Description **Cambridge University Press, 2011. Softcover. Book Condition: New. 2nd edition. 18 x 24 cm. This book explains how computer software is designed to perform the tasks required for sophisticated statistical analysis. For statisticians, it examines the nitty-gritty computational problems behind statistical methods. For mathematicians and computer scientists, it looks at the application of mathematical tools to statistical problems. The first half of the book offers a basic background in numerical analysis that emphasizes issues important to statisticians. The next several chapters cover a broad array of statistical tools, such as maximum likelihood and nonlinear regression. The author also treats the application of numerical tools; numerical integration and random number generation are explained in a unified manner reflecting complementary views of Monte Carlo methods. Each chapter contains exercises that range from simple questions to research problems. Most of the examples are accompanied by demonstration and source code available from the author`s website. New in this second edition are demonstrations coded in R, as well as new sections on linear programming and the Nelderâ€"Mead search algorithm. Contents 1. Algorithms and computers 2. Computer arithmetic 3. Matrices and linear equations 4. More methods for solving linear equations 5. Least squares 6. Eigenproblems 7. Functions: interpolation, smoothing and approximation 8. Introduction to optimization and nonlinear equations 9. Maximum likelihood and nonlinear regression 10. Numerical integration and Monte Carlo methods 11. Generating random variables from other distributions 12. Statistical methods for integration and Monte Carlo 13. Markov chain Monte Carlo methods 14. Sorting and fast algorithms. Printed Pages: 458. Bookseller Inventory # 39475

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