Exploring Posterior Distributions Using Markov Chains

Abstract

Several Markov chain-based methods are available for sampling from a posterior distribution. Two important examples are the Gibbs sampler and the Metropolis algorithm. In addition, several strategies are available for constructing hybrid algorithms. This paper outlines some of the strategies that are available, and discusses some theoretical and practical issues in the use of these strategies. In addition, some preliminary efforts to use Markov chains to control dynamic graphics for exploring higher-dimensional posterior distributions are outlined.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1992
Accession Number
ADP007214

Entities

People

  • Luke Tierney

Organizations

  • University of Minnesota

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Computer Languages
  • Computer Science
  • Data Science
  • Information Science
  • Markov Chains
  • Monte Carlo Method
  • Network Science
  • Operations Research
  • Probability
  • Random Walk
  • Sampling
  • Simulations
  • Statistical Algorithms
  • Statistics
  • Three Dimensional

Fields of Study

  • Mathematics

Readers

  • Mathematical Modeling and Probability Theory.
  • Statistical inference.
  • Systems Analysis and Design