Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling

Abstract

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.

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Document Details

Document Type
Technical Report
Publication Date
Sep 06, 1989
Accession Number
ADA212630

Entities

People

  • Adrian F. Smith
  • Alan E. Gelfand
  • Amy Racine-poon
  • Susan E. Hills

Organizations

  • Stanford University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Bayesian Inference
  • Bayesian Networks
  • Computational Science
  • Data Analysis
  • Data Mining
  • Data Science
  • Data Sets
  • Information Processing
  • Information Science
  • Monte Carlo Method
  • Sampling
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Inference
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms