Handbook of Markov Chain Monte Carlo
Seiten
2011
Chapman & Hall/CRC (Verlag)
978-1-4200-7941-8 (ISBN)
Chapman & Hall/CRC (Verlag)
978-1-4200-7941-8 (ISBN)
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Brings together the major advances that have occurred over the years while incorporating enough introductory material for new users of Markov Chain Monte Carlo. Along with coverage of the theoretical foundations and algorithmic and computational methodology, this handbook also includes case studies that demonstrate the application of MCMC methods.
Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisheries science and economics. The wide-ranging practical importance of MCMC has sparked an expansive and deep investigation into fundamental Markov chain theory.
The Handbook of Markov Chain Monte Carlo provides a reference for the broad audience of developers and users of MCMC methodology interested in keeping up with cutting-edge theory and applications. The first half of the book covers MCMC foundations, methodology, and algorithms. The second half considers the use of MCMC in a variety of practical applications including in educational research, astrophysics, brain imaging, ecology, and sociology.
The in-depth introductory section of the book allows graduate students and practicing scientists new to MCMC to become thoroughly acquainted with the basic theory, algorithms, and applications. The book supplies detailed examples and case studies of realistic scientific problems presenting the diversity of methods used by the wide-ranging MCMC community. Those familiar with MCMC methods will find this book a useful refresher of current theory and recent developments.
Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisheries science and economics. The wide-ranging practical importance of MCMC has sparked an expansive and deep investigation into fundamental Markov chain theory.
The Handbook of Markov Chain Monte Carlo provides a reference for the broad audience of developers and users of MCMC methodology interested in keeping up with cutting-edge theory and applications. The first half of the book covers MCMC foundations, methodology, and algorithms. The second half considers the use of MCMC in a variety of practical applications including in educational research, astrophysics, brain imaging, ecology, and sociology.
The in-depth introductory section of the book allows graduate students and practicing scientists new to MCMC to become thoroughly acquainted with the basic theory, algorithms, and applications. The book supplies detailed examples and case studies of realistic scientific problems presenting the diversity of methods used by the wide-ranging MCMC community. Those familiar with MCMC methods will find this book a useful refresher of current theory and recent developments.
Steve Brooks, Andrew Gelman, Galin Jones, Xiao-Li Meng
Foundations, Methodology, and Algorithms. Applications.
| Erscheint lt. Verlag | 17.5.2011 |
|---|---|
| Reihe/Serie | Chapman & Hall/CRC Handbooks of Modern Statistical Methods |
| Zusatzinfo | 16 Tables, black and white; 125 Illustrations, black and white |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Gewicht | 1600 g |
| Themenwelt | Mathematik / Informatik ► Mathematik |
| Naturwissenschaften ► Biologie | |
| ISBN-10 | 1-4200-7941-7 / 1420079417 |
| ISBN-13 | 978-1-4200-7941-8 / 9781420079418 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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