“The book is suitable as a textbook for one-semestercourses on Monte Carlo methods, offered at the advance postgraduatelevels.” (Mathematical Reviews, 1 December2012)
"Researchers working in the field of applied statistics will profitfrom this easy-to-access presentation. Further illustration is doneby discussing interesting examples and relevant applications. Thevaluable reference list includes technical reports which are hardto and by searching in public data bases." (Zentralblatt MATH,2011)
"This book can be used as a textbook or a reference book for aone-semester graduate course in statistics, computational biology,engineering, and computer sciences. Applied or theoreticalresearchers will also find this book beneficial." (Breitbart.com:Business Wire , 1 February 2011)
|"The Markov Chain Monte Carlo methodhas now become the dominant methodology for solving many classes ofcomputational problems in science and technology." (SciTech BookNews, December 2010)|
From the Back Cover
Markov Chain Monte Carlo (MCMC) methods are now an indispensabletool in scientific computing. This book discusses recentdevelopments of MCMC methods with an emphasis on those making useof past sample information during simulations. The applicationexamples are drawn from diverse fields such as bioinformatics,machine learning, social science, combinatorial optimization, andcomputational physics.
- Expanded coverage of the stochastic approximation Monte Carloand dynamic weighting algorithms that are essentially immune tolocal trap problems.
- A detailed discussion of the Monte Carlo Metropolis-Hastingsalgorithm that can be used for sampling from distributions withintractable normalizing constants.
- Up-to-date accounts of recent developments of the Gibbssampler.
- Comprehensive overviews of the population-based MCMC algorithmsand the MCMC algorithms with adaptive proposals.
- Accompanied by a supporting website featuring datasets used inthe book, along with codes used for some simulation examples.
This book can be used as a textbook or a reference book for aone-semester graduate course in statistics, computational biology,engineering, and computer sciences. Applied or theoreticalresearchers will also find this book beneficial.