Bayesian Computation Via the Gibbs Sampler and Related Markov Chain Monte Carlo Methods
- 1 September 1993
- journal article
- research article
- Published by Oxford University Press (OUP) in Journal of the Royal Statistical Society Series B: Statistical Methodology
- Vol. 55 (1) , 3-23
- https://doi.org/10.1111/j.2517-6161.1993.tb01466.x
Abstract
SUMMARY: The use of the Gibbs sampler for Bayesian computation is reviewed and illustrated in the context of some canonical examples. Other Markov chain Monte Carlo simulation methods are also briefly described, and comments are made on the advantages of sample-based approaches for Bayesian inference summaries.This publication has 32 references indexed in Scilit:
- Nonparametric Bayesian Bioassay with Prior Constraints on the Shape of the Potency CurveBiometrika, 1993
- Bayesian Analysis of Constrained Parameter and Truncated Data Problems Using Gibbs SamplingJournal of the American Statistical Association, 1992
- Bayesian computational methodsPhilosophical Transactions A, 1991
- Efficient generation of random variates via the ratio-of-uniforms methodStatistics and Computing, 1991
- Nonparametric Bayesian bioassay including ordered polytomous responseBiometrika, 1991
- Structural Image Restoration through Deformable TemplatesJournal of the American Statistical Association, 1991
- Gibbs sampling for marginal posterior expectationsCommunications in Statistics - Theory and Methods, 1991
- Illustration of Bayesian Inference in Normal Data Models Using Gibbs SamplingJournal of the American Statistical Association, 1990
- Sampling-Based Approaches to Calculating Marginal DensitiesJournal of the American Statistical Association, 1990
- The Calculation of Posterior Distributions by Data AugmentationJournal of the American Statistical Association, 1987