"The exposition is quite clear, intuitive, and is a useful complement to more abstract treatises on stochastic calculus and simulation." (MathSciNet, 1 December 2015) Careful presentation and examples make this book accessible to a wide range of students and suitable for self-study or as the basis of a taught course. (Zentralblatt MATH, 1 March 2014) Statistical computing in its broadest sense is an ever-growing field far too extensive to be covered in a single text. The current book has a far more manageable scope, notwithstanding its title. Its focus is on the use of Monte Carlo methods to simulate random systems and explore statistical models. (Mathematical Association of America, 1 January 2014)
Reseña del editor:A comprehensive introduction to sampling-based methods in statistical computing The use of computers in mathematics and statistics has opened up a wide range of techniques for studying otherwise intractable problems. Sampling-based simulation techniques are now an invaluable tool for exploring statistical models. This book gives a comprehensive introduction to the exciting area of sampling-based methods. An Introduction to Statistical Computing introduces the classical topics of random number generation and Monte Carlo methods. It also includes some advanced methods such as the reversible jump Markov chain Monte Carlo algorithm and modern methods such as approximate Bayesian computation and multilevel Monte Carlo techniques An Introduction to Statistical Computing: * Fully covers the traditional topics of statistical computing. * Discusses both practical aspects and the theoretical background. * Includes a chapter about continuous-time models. * Illustrates all methods using examples and exercises. * Provides answers to the exercises (using the statistical computing environment R); the corresponding source code is available online. * Includes an introduction to programming in R. This book is mostly self-contained; the only prerequisites are basic knowledge of probability up to the law of large numbers. Careful presentation and examples make this book accessible to a wide range of students and suitable for self-study or as the basis of a taught course
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Descripción John Wiley Sons Inc, United States, 2013. Hardback. Condición: New. 1. Auflage. Language: English . Brand New Book. A comprehensive introduction to sampling-based methods in statistical computing The use of computers in mathematics and statistics has opened up a wide range of techniques for studying otherwise intractable problems. Sampling-based simulation techniques are now an invaluable tool for exploring statistical models. This book gives a comprehensive introduction to the exciting area of sampling-based methods. An Introduction to Statistical Computing introduces the classical topics of random number generation and Monte Carlo methods. It also includes some advanced methods such as the reversible jump Markov chain Monte Carlo algorithm and modern methods such as approximate Bayesian computation and multilevel Monte Carlo techniques An Introduction to Statistical Computing: * Fully covers the traditional topics of statistical computing. * Discusses both practical aspects and the theoretical background. * Includes a chapter about continuous-time models. * Illustrates all methods using examples and exercises. * Provides answers to the exercises (using the statistical computing environment R); the corresponding source code is available online. * Includes an introduction to programming in R. This book is mostly self-contained; the only prerequisites are basic knowledge of probability up to the law of large numbers. Careful presentation and examples make this book accessible to a wide range of students and suitable for self-study or as the basis of a taught course. Nº de ref. del artículo: AAH9781118357729
Descripción Condición: New. Nº de ref. del artículo: 15779263-n
Descripción John Wiley Sons Inc, United States, 2013. Hardback. Condición: New. 1. Auflage. Language: English . Brand New Book. A comprehensive introduction to sampling-based methods in statistical computing The use of computers in mathematics and statistics has opened up a wide range of techniques for studying otherwise intractable problems. Sampling-based simulation techniques are now an invaluable tool for exploring statistical models. This book gives a comprehensive introduction to the exciting area of sampling-based methods. An Introduction to Statistical Computing introduces the classical topics of random number generation and Monte Carlo methods. It also includes some advanced methods such as the reversible jump Markov chain Monte Carlo algorithm and modern methods such as approximate Bayesian computation and multilevel Monte Carlo techniques An Introduction to Statistical Computing: * Fully covers the traditional topics of statistical computing. * Discusses both practical aspects and the theoretical background. * Includes a chapter about continuous-time models. * Illustrates all methods using examples and exercises. * Provides answers to the exercises (using the statistical computing environment R); the corresponding source code is available online. * Includes an introduction to programming in R. This book is mostly self-contained; the only prerequisites are basic knowledge of probability up to the law of large numbers. Careful presentation and examples make this book accessible to a wide range of students and suitable for self-study or as the basis of a taught course. Nº de ref. del artículo: AAH9781118357729
Descripción John Wiley and Sons. Condición: New. Brand New. Nº de ref. del artículo: 1118357728
Descripción Wiley 2013-10-18, Chichester, 2013. hardback. Condición: New. Nº de ref. del artículo: 9781118357729
Descripción Wileyand#8211;Blackwell, 2013. HRD. Condición: New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Nº de ref. del artículo: FW-9781118357729
Descripción Wiley, 2013. Condición: New. A comprehensive introduction to sampling-based methods in statistical computing The use of computers in mathematics and statistics has opened up a wide range of techniques for studying otherwise intractable problems. Sampling-based simulation techniques are now an invaluable tool for exploring statistical models. Series: Wiley Series in Computational Statistics. Num Pages: 396 pages, black & white illustrations, black & white tables, figures. BIC Classification: PBT; UYM. Category: (P) Professional & Vocational. Dimension: 160 x 230 x 23. Weight in Grams: 636. . 2013. 1st Edition. Hardcover. . . . . . Nº de ref. del artículo: V9781118357729
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Descripción Wiley-Blackwell, 2013. Condición: New. book. Nº de ref. del artículo: ria9781118357729_rkm
Descripción Wiley Series in Computational Statistics, 2013. Condición: New. Nº de ref. del artículo: EH9781118357729