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.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!