9781118357729 - An Introduction to Statistical Computing: A Simulation-based Approach di Voss, Jochen (12 risultati)

hardcover. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!

Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Hardcover. Condizione: new. Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condizione: As New. Unread book in perfect condition.

Condizione: New. Brand new! Please provide a physical shipping address.

Condizione: 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. . . . .

Condizione: New.

Condizione: 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. . . . . Books ship from the US and Ireland.

Hardcover. Condizione: new. Hardcover. 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. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condizione: new. Hardcover. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.