Abstract
Summary 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.
Keywords
Gibbs samplingMarkov chain Monte CarloMetropolis–Hastings algorithmBayesian probabilityComputer scienceSampling (signal processing)Slice samplingMarkov chainAlgorithmConjugate priorRejection samplingMathematicsHybrid Monte CarloStatisticsArtificial intelligencePrior probability
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Publication Info
- Year
- 1999
- Type
- article
- Volume
- 61
- Issue
- 2
- Pages
- 331-344
- Citations
- 356
- Access
- Closed
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Cite This
P. Damlen,
Jon Wakefield,
Stephen Walker
(1999).
Gibbs Sampling for Bayesian Non-Conjugate and Hierarchical Models by Using Auxiliary Variables.
Journal of the Royal Statistical Society Series B (Statistical Methodology)
, 61
(2)
, 331-344.
https://doi.org/10.1111/1467-9868.00179
Identifiers
- DOI
- 10.1111/1467-9868.00179