E-Book, Englisch, 374 Seiten, E-Book
Liang / Liu / Carroll Advanced Markov Chain Monte Carlo Methods
1. Auflage 2011
ISBN: 978-1-119-95680-8
Verlag: John Wiley & Sons
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
Learning from Past Samples
E-Book, Englisch, 374 Seiten, E-Book
Reihe: Wiley Series in Computational Statistics
ISBN: 978-1-119-95680-8
Verlag: John Wiley & Sons
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
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.
Key Features:
* 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.
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.
