Gibbs sampling for Bayesian non‐conjugate and hierarchical models by using auxiliary variables

P. Damlen, J. Wakefield and S. Walker

Journal of the Royal Statistical Society Series B, 1999, vol. 61, issue 2, 331-344

Abstract: We demonstrate the use of auxiliary (or latent) variables for sampling non‐standard densities which arise in the context of the Bayesian analysis of non‐conjugate and hierarchical models by using a Gibbs sampler. Their strategic use can result in a Gibbs sampler having easily sampled full conditionals. We propose such a procedure to simplify or speed up the Markov chain Monte Carlo algorithm. The strength of this approach lies in its generality and its ease of implementation. The aim of the paper, therefore, is to provide an alternative sampling algorithm to rejection‐based methods and other sampling approaches such as the Metropolis–Hastings algorithm.

Date: 1999
References: Add references at CitEc
Citations: View citations in EconPapers (46)

Downloads: (external link)
https://doi.org/10.1111/1467-9868.00179

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssb:v:61:y:1999:i:2:p:331-344

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9868

Access Statistics for this article

Journal of the Royal Statistical Society Series B is currently edited by P. Fryzlewicz and I. Van Keilegom

More articles in Journal of the Royal Statistical Society Series B from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().