Abstract
There are two major challenges involved in advanced Bayesian computation. These are how to sample from posterior distributions and how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples. Several books, including Tanner (1996), Gilks, Richardson, and Spiegclhaltcr (1996), Gamerman (1997), Robert and Casella (1999), and Gelfand and Smith (2000), cover the development of MCMC sampling. Therefore, this book will provide only a quick but sufficient introduction to recently developed MCMC sampling techniques. In particular, the book will discuss several recently developed and useful computational tools in MCMC sampling which may not be presented in other existing MCMC books including those mentioned above.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer Science+Business Media New York
About this chapter
Cite this chapter
Chen, MH., Shao, QM., Ibrahim, J.G. (2000). Introduction. In: Monte Carlo Methods in Bayesian Computation. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1276-8_1
Download citation
DOI: https://doi.org/10.1007/978-1-4612-1276-8_1
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4612-7074-4
Online ISBN: 978-1-4612-1276-8
eBook Packages: Springer Book Archive