Posterior approximation with the Gibbs sampler

Part of the Springer Texts in Statistics book series (STS)


For many multiparameter models the joint posterior distribution is nonstandard and difficult to sample from directly. However, it is often the case that it is easy to sample from the full conditional distribution of each parameter. In such cases, posterior approximation can be made with the Gibbs sampler, an iterative algorithm that constructs a dependent sequence of parameter values whose distribution converges to the target joint posterior distribution. In this chapter we outline the Gibbs sampler in the context of the normal model with a semiconjugate prior distribution, and discuss how well the method is able to approximate the posterior distribution.


Posterior Distribution Markov Chain Monte Carlo Gibbs Sampler Discrete Approximation Target Distribution 
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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of WashingtonSeattleUSA

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