Advanced Markov Chain Monte Carlo Methods Learning from Past Samples |
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| Author:
| Liang, Faming
Liu, Chuanhai
Carroll, Raymond |
| Series title: | Wiley Series in Computational Statistics Ser. |
| ISBN: | 978-1-119-95680-8 |
| Publication Date: | Jul 2011 |
| Publisher: | John Wiley & Sons, Incorporated
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| Book Format: | Digital download |
| List Price: | Contact Supplier contact
Contact Supplier contact
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Book Description:
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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:
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.