Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisheries science and economics. The wide-ranging practical importance of MCMC has sparked an expansive and deep investigation into fundamental Markov chain theory.
The Handbook of Markov Chain Monte Carlo
provides a reference for the broad audience of developers and users of
MCMC methodology interested in keeping up with cutting-edge theory and
applications. The first half of the book covers MCMC foundations,
methodology, and algorithms. The second half considers the use of MCMC
in a variety of practical applications including in educational
research, astrophysics, brain imaging, ecology, and sociology. The
in-depth introductory section of the book allows graduate students and
practicing scientists new to MCMC to become thoroughly acquainted with
the basic theory, algorithms, and applications. The book supplies
detailed examples and case studies of realistic scientific problems
presenting the diversity of methods used by the wide-ranging MCMC
community. Those familiar with MCMC methods will find this book a useful
refresher of current theory and recent developments.