Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling

@article{Gelfand1990IllustrationOB,
  title={Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling},
  author={Alan E. Gelfand and Susan E. Hills and Amy Racine‐Poon and Adrian F. M. Smith},
  journal={Journal of the American Statistical Association},
  year={1990},
  volume={85},
  pages={972-985}
}
Abstract The use of the Gibbs sampler as a method for calculating Bayesian marginal posterior and predictive densities is reviewed and illustrated with a range of normal data models, including variance components, unordered and ordered means, hierarchical growth curves, and missing data in a crossover trial. In all cases the approach is straightforward to specify distributionally and to implement computationally, with output readily adapted for required inference summaries. 
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