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.
Über den Autor
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.
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