• Ming-Hui Chen
  • Qi-Man Shao
  • Joseph G. Ibrahim
Part of the Springer Series in Statistics book series (SSS)


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.


Markov Chain Monte Carlo Monte Carlo Markov Chain Monte Carlo Sampling Reversible Jump Markov Chain Monte Carlo Bayesian Variable Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2000

Authors and Affiliations

  • Ming-Hui Chen
    • 1
  • Qi-Man Shao
    • 2
  • Joseph G. Ibrahim
    • 3
  1. 1.Department of Mathematical SciencesWorcester Polytechnic InstituteWorcesterUSA
  2. 2.Department of MathematicsUniversity of OregonEugeneUSA
  3. 3.Department of BiostatisticsHarvard School of Public Health and Dana-Farber Cancer InstituteBostonUSA

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