Advanced Markov Chain Monte Carlo Methods

von

Liang, FamingLiu, ChuanhaiCarroll, Raymond

E-BookEPUB2 - DRM Adobe / EPUBE-Book

374 Seiten

Englisch

Wileyerschienen am05.07.20111. Auflage

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.mehr

Verfügbare Formate

BuchGebunden

EUR143,50

E-BookPDF2 - DRM Adobe / Adobe Ebook ReaderE-Book

EUR96,99

E-BookEPUB2 - DRM Adobe / EPUBE-Book

EUR96,99

Auf dieser Seite:

EUR96,99

In den Warenkorb

Produkt

KlappentextMarkov 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.

Details

Weitere ISBN/GTIN9781119956808

ProduktartE-Book

EinbandartE-Book

FormatEPUB

Format Hinweis2 - DRM Adobe / EPUB

FormatFormat mit automatischem Seitenumbruch (reflowable)

Verlag

Wiley

Erscheinungsjahr2011

Erscheinungsdatum05.07.2011

Auflage1. Auflage