Easily Verifiable Conditions for the Convergence of the Markov Chain Monte Carlo Method.

Abstract

The Markov Chain Chain Monte Carlo (MCMC) method, which is a special case of the Gibbs sampler, is a very powerful method to simulate from complicated distributions arising in many contexts, including image analysis, computational Bayesian analysis, and so on. Existing results that ensure that this method will converge involve conditions which are difficult to verify in practice, and most practitioners, convinced that their particular problem will not be pathological and give up verifying altogether. This paper gives a new set of sufficient conditions which are easy to verify in most applications.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1995
Accession Number
ADA308874

Entities

People

  • Jayaram Sethuraman

Organizations

  • Florida State University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Bayesian Networks
  • Blood Groups
  • Computational Science
  • Data Science
  • Ergodic Processes
  • Human Population
  • Information Science
  • Markov Chains
  • Monte Carlo Method
  • Probability
  • Probability Distributions
  • Random Variables
  • Simulations
  • Statistical Inference
  • Statistics
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Statistical inference.
  • Strategic Security Studies
  • Systems Analysis and Design

Technology Areas

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