A Proof of Convergence of the Markov Chain Simulation Method

Abstract

The Markov chain simulation method has been successfully used in many problems, including some that arise in Bayesian statistics. We give a self- contained proof of the convergence of this method in general state spaces under conditions that are easy to verify. We also provide a result giving a geometric rate of convergence. Successive substitution sampling, calculation of posterior distributions, ergodic theorem.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1992
Accession Number
ADA255456

Entities

People

  • Hani Doss
  • Jayaram Sethuraman
  • Krishna B. Athreya

Organizations

  • Florida State University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Classification
  • Ergodic Processes
  • Markov Chains
  • Military Research
  • Monte Carlo Method
  • New York
  • Probability
  • Probability Distributions
  • Random Variables
  • Sampling
  • Security
  • Simulations
  • Statistical Analysis
  • Statistics
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Mathematical Modeling and Probability Theory.
  • Statistical inference.

Technology Areas

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