Téléchargez le livre : Advanced Markov Chain Monte Carlo Methods

Advanced Markov Chain Monte Carlo Methods

Learning from Past Samples de

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Éditeur :

Wiley


Collection :

Wiley Series in Computational Statistics

Paru le : 2011-07-05

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Description
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.
Pages
384 pages
Collection
Wiley Series in Computational Statistics
Parution
2011-07-05
Marque
Wiley
EAN papier
9780470748268
EAN PDF
9780470669730
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
384
Taille du fichier
3830 Ko
Prix
112,83 €
EAN EPUB
9781119956808
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
384
Taille du fichier
20572 Ko
Prix
112,83 €

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