Advanced Markov Chain Monte Carlo Methods / Wiley Series in Computational Statistics (PDF)
Learning from Past Samples
(Sprache: Englisch)
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...
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...
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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:
* 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.
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.
Inhaltsverzeichnis zu „Advanced Markov Chain Monte Carlo Methods / Wiley Series in Computational Statistics (PDF)“
Preface Acknowledgements List of Figures List of Tables 1 Bayesian Inference and Markov chain Monte Carlo 1.1 Bayes 1.2 Bayes output 1.3 Monte Carlo Integration 1.4 Random variable generation 1.5 Markov chain Monte Carlo Exercises 2 The Gibbs sampler 2.1 The Gibbs sampler 2.2 Data Augmentation 2.3 Implementation strategies and acceleration methods 2.4 Applications Exercises 3 The Metropolis-Hastings Algorithm 3.1 The Metropolis-Hastings Algorithm 3.2 Some Variants of the Metropolis-Hastings Algorithm 3.3 Reversible Jump MCMC Algorithm for Bayesian Model Selection Problems 3.4 Metropolis-within-Gibbs Sampler for ChIP-chip Data Analysis Exercises 4 Auxiliary Variable MCMC Methods 4.1 Simulated Annealing 4.2 Simulated Tempering 4.3 Slice Sampler 4.4 The Swendsen-Wang Algorithm 4.5 The Wolff Algorithm 4.6 The Møller algorithm 4.7 The Exchange Algorithm 4.8 Double MH Sampler 4.9 Monte Carlo MH Sampler 4.10 Applications Exercises 5 Population-Based MCMC Methods 5.1 Adaptive Direction Sampling 5.2 Conjugate Gradient Monte Carlo 5.3 Sample Metropolis-Hastings Algorithm 5.4 Parallel Tempering 5.5 Evolutionary Monte Carlo 5.6 Sequential Parallel Tempering for Simulation of High Dimensional Systems 5.7 Equi-Energy Sampler 5.8 Applications Forecasting Exercises 6 Dynamic Weighting 6.1 Dynamic Weighting 6.2 Dynamically Weighted Importance Sampling 6.3 Monte Carlo Dynamically Weighted Importance Sampling 6.4 Sequentially Dynamically Weighted Importance Sampling Exercises 7 Stochastic Approximation Monte Carlo 7.1 Multicanonical Monte Carlo 7.2 1/k-Ensemble Sampling 7.3 Wang-Landau Algorithm 7.4 Stochastic Approximation Monte Carlo 7.5 Applications of Stochastic Approximation Monte Carlo 7.6 Variants of Stochastic Approximation Monte Carlo 7.7 Theory of Stochastic Approximation Monte Carlo 7.8 Trajectory Averaging: Toward the Optimal Convergence Rate Exercises 8 Markov Chain Monte Carlo with Adaptive Proposals 8.1 Stochastic Approximation-based Adaptive Algorithms 8.2 Adaptive
... mehr
Independent Metropolis-Hastings Algorithms 8.3 Regeneration-based Adaptive Algorithms 8.4 Population-based Adaptive Algorithms Exercises References Index
... weniger
Autoren-Porträt von Faming Liang, Chuanhai Liu, Raymond Carroll
Faming Liang, Associate Professor, Department of Statistics, Texas A&M University.Chuanhai Liu, Professor, Department of Statistics, Purdue University.
Raymond J. Carroll, Distinguished Professor, Department of Statistics, Texas A&M University.
Bibliographische Angaben
- Autoren: Faming Liang , Chuanhai Liu , Raymond Carroll
- 2010, 1. Auflage, 384 Seiten, Englisch
- Verlag: John Wiley & Sons
- ISBN-10: 047066973X
- ISBN-13: 9780470669730
- Erscheinungsdatum: 09.06.2010
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