Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling

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 an ordered means hierarchial growth curves, and missing data in a cross-over trial. in all cases the approach is straight forward to specify distributionally, trivial to implement computationally, with output readily adapted for required inference summaries.