Illustration of Bayesian inference in normal data models using Gibbs sampling

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Gelfand, AE; Hills, SE; Racine-Poon, A; Smith, AFM

Published Date

  • January 1, 1990

Published In

Volume / Issue

  • 85 / 412

Start / End Page

  • 972 - 985

Electronic International Standard Serial Number (EISSN)

  • 1537-274X

International Standard Serial Number (ISSN)

  • 0162-1459

Digital Object Identifier (DOI)

  • 10.1080/01621459.1990.10474968

Citation Source

  • Scopus