Illustration of Bayesian inference in normal data models using Gibbs sampling
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. © 1990 Taylor & Francis Group, LLC.
Gelfand, AE; Hills, SE; Racine-Poon, A; Smith, AFM
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