Advanced Markov Chain Monte Carlo Methods (eBook)

Learning from Past Samples

Faming Liang, Chuanhai Liu, Raymond Carroll (Autoren)

eBook Download: EPUB

2011 | 1. Auflage
374 Seiten
John Wiley & Sons (Verlag)
978-1-119-95680-8 (ISBN)

Infos

Markov Chain Monte Carlo (MCMC) methods are now an indispensable
tool in scientific computing. This book discusses recent
developments of MCMC methods with an emphasis on those making use
of past sample information during simulations. The application
examples are drawn from diverse fields such as bioinformatics,
machine learning, social science, combinatorial optimization, and
computational physics.

Key Features:

* Expanded coverage of the stochastic approximation Monte Carlo
and dynamic weighting algorithms that are essentially immune to
local trap problems.

* A detailed discussion of the Monte Carlo Metropolis-Hastings
algorithm that can be used for sampling from distributions with
intractable normalizing constants.

* Up-to-date accounts of recent developments of the Gibbs
sampler.

* Comprehensive overviews of the population-based MCMC algorithms
and the MCMC algorithms with adaptive proposals.

This book can be used as a textbook or a reference book for a
one-semester graduate course in statistics, computational biology,
engineering, and computer sciences. Applied or theoretical
researchers will also find this book beneficial.

Autor

Faming Liang, Associate Professor, Department of Statistics, Texas A&M University. Chuanhai Liu, Professor, Department of Statistics, Purdue University. Raymond J. Carroll, Distinguished Professor, Department of Statistics, Texas A&M University.

Rezensionen

"The book is suitable as a textbook for one-semester courses on Monte Carlo methods, offered at the advance postgraduate levels." (Mathematical Reviews, 1 December 2012)

"Researchers working in the field of applied statistics will profit from this easy-to-access presentation. Further illustration is done by discussing interesting examples and relevant applications. The valuable reference list includes technical reports which are hard to and by searching in public data bases." (Zentralblatt MATH, 2011)

"This book can be used as a textbook or a reference book for a one-semester graduate course in statistics, computational biology, engineering, and computer sciences. Applied or theoretical researchers will also find this book beneficial." (Breitbart.com: Business Wire , 1 February 2011)

"The Markov Chain Monte Carlo method has now become the dominant methodology for solving many classes of computational problems in science and technology." (SciTech Book News, December 2010)

Produkt-Details

• Erscheint lt. Verlag: 5.7.2011
• Reihe/Serie: Wiley Series in Computational Statistics
• Reihe/Serie: Wiley Series in Computational Statistics
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Analysis
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Technik
• Schlagworte: Bayesian analysis • Bayes-Verfahren • biometrics • Biometrie • Computational & Graphical Statistics • Rechnergestützte u. graphische Statistik • Rechnergestützte u. graphische Statistik • Statistics • Statistik • Wahrscheinlichkeitsrechnung
• ISBN-10: 1-119-95680-3 / 1119956803
• ISBN-13: 978-1-119-95680-8 / 9781119956808