LOCAL WEAK CONSISTENCY OF MARKOV CHAIN MONTE CARLO METHODS WITH APPLICATION TO MIXTURE MODEL

• Kamatani Kengo

  Graduate School of Engineering Science, Osaka University

Abstract

Markov chain Monte Calro methods (MCMC) are commonly used in Bayesian statistics. In the last twenty years, many results have been established for the calculation of the exact convergence rate of MCMC methods. We introduce another rate of convergence for MCMC methods by approximation techniques. This rate can be obtained by the convergence of the Markov chain to a diffusion process. We apply it to a simple mixture model and obtain its convergence rate. Numerical simulations are performed to illustrate the effect of the rate.

Journal

• Bulletin of informatics and cybernetics

  Bulletin of informatics and cybernetics 45 103-123, 2013-12

  Research Association of Statistical Sciences

Keywords

• Markov chain Monte Carlo
• Asymptotic Normality
• Diffusion process

Details

• CRID: 1390009224849124864
• NII Article ID: 120005703633
• NII Book ID: AA10634475
• DOI: 10.5109/1563534
• ISSN: 2435743X
• ISSN: 0286522X
• HANDLE: 2324/1563534
• Text Lang: en
• Data Source:
  • JaLC
  • IRDB
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Allowed