Introduction

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

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.

Keywords

Placebo Covariance Turkey Zidovudine 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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

Personalised recommendations