On Markov Chain Monte Carlo Acceleration

Abstract

Markov chain Monte Carlo (MCMC) methods are currently enjoying a surge of interest within the statistical community. The goal of this work is to formalize and support two distinct adaptive strategies which typically accelerate the convergence of a MCMC algorithm. One approach is through resampling; the other incorporates adaptive switching of the transition kernel. Support is both by analytic arguments and simulation study. Application is envisioned in low dimensional but non-trivial problems. Two pathological illustrations are presented. Connections with reparametrization are discussed as well as possible difficulties with infinitely often adaptation.

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Document Details

Document Type
Technical Report
Publication Date
Apr 04, 1994
Accession Number
ADA279393

Entities

People

  • Alan E. Gelfand
  • Sujit K. Sahu

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Convergence
  • Data Mining
  • Data Science
  • Information Science
  • Markov Chains
  • Markov Processes
  • Maximum Likelihood Estimation
  • Monte Carlo Method
  • Numerical Analysis
  • Probability
  • Sampling
  • Simulations
  • Statistical Algorithms
  • Statistics
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Economics
  • Statistical inference.