Bayesian Analysis of Constrained Parameter and Truncated Data Problems

Abstract

Bayesian analysis of constrained parameter and truncated data problems is complicated by the seeming need for, typically multidimensional, numerical integrations over awkwardly defined regions. This paper illustrates how the Gibbs sampler approach to Bayesian calculation (Gelfand and Smith, 1990) avoids these difficulties and leads to straightforwardly implemented procedures, even for apparently very complicated model forms.

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Document Details

Document Type
Technical Report
Publication Date
Jan 04, 1991
Accession Number
ADA231080

Entities

People

  • A. E. Gelfand
  • A. F. Smith
  • T. M. Lee

Organizations

  • Stanford University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

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

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

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