One attractive method for constructing an MCMC algorithm is Gibbs sampling, introduced in Chapter 6. To slightly generalize our earlier discussion, suppose that we partition the parameter vector of interest into \(p\) components \(\theta = (\theta_1, \ldots, \theta_p)\), where \(\theta_k\) may consist of a vector of parameters. The MCMC algorithm is implemented by sampling in turn from the \(p\) conditional posterior distributions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag New York
About this chapter
Cite this chapter
Albert, J. (2009). Gibbs Sampling. In: Bayesian Computation with R. Springer, New York, NY. https://doi.org/10.1007/978-0-387-92298-0_10
Download citation
DOI: https://doi.org/10.1007/978-0-387-92298-0_10
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-92297-3
Online ISBN: 978-0-387-92298-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)