Gibbs Sampling for Marginal Posterior Expectations

Abstract

In earlier work (Gelfand and Smith, 1990 and Gelfand et al, 1989) a sampling based approach using the Gibbs sampler was offered as a means for developing marginal posterior densities for a wide range of Bayesian problems several of which were previously inaccessible. Our purpose here is two-fold. First we flesh out the implementation of this approach for calculation of arbitrary expectations of interest. Secondly we offer comparison with perhaps the most prominent approach for calculating posterior expectations, analytic approximation involving application of the LaPlace method. Several illustrative examples are discussed as well. Clear advantages for the sampling based approach emerge.

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Document Details

Document Type
Technical Report
Publication Date
Nov 19, 1991
Accession Number
ADA243212

Entities

People

  • Adrian F. Smith
  • Alan E. Gelfand

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

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

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Calculus or Mathematical Analysis
  • Systems Analysis and Design

Technology Areas

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