An Iterative Monte Carlo Method for Nonconjugate Bayesian Analysis

Abstract

The Gibbs sampler has been proposed as a general method for Bayesian calculation in Gelfand and Smith (1990). However experience to date is almost exclusively in applications assuming conjugacy where implementation is reasonably straightforward. This paper describes a tailored rejection method approach for implementation of the Gibbs sampler when nonconjugate structure is present. Several challenging applications are presented for illustration.

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Document Details

Document Type
Technical Report
Publication Date
Sep 07, 1992
Accession Number
ADA255991

Entities

People

  • Alan E. Gelfand
  • Bradley P. Carlin

Organizations

  • Stanford University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Bayesian Inference
  • Bayesian Networks
  • Computational Science
  • Data Mining
  • Data Science
  • Information Science
  • Mathematics
  • Monte Carlo Method
  • Probability
  • Random Variables
  • Rejection
  • Sampling
  • Statistical Algorithms
  • Statistics
  • United States

Fields of Study

  • Mathematics

Readers

  • Statistical inference.
  • Systems Analysis and Design

Technology Areas

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