Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If you need an accessible version of this item please contact JSTOR User Support

On Markov Chain Monte Carlo Acceleration

Alan E. Gelfand and Sujit K. Sahu
Journal of Computational and Graphical Statistics
Vol. 3, No. 3 (Sep., 1994), pp. 261-276
DOI: 10.2307/1390911
Stable URL: http://www.jstor.org/stable/1390911
Page Count: 16
  • Download ($14.00)
  • Cite this Item
If you need an accessible version of this item please contact JSTOR User Support
On Markov Chain Monte Carlo Acceleration
Preview not available

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 that typically accelerate the convergence of an 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 nontrivial problems. Two pathological illustrations are presented. Connections with reparmeterization are discussed as well as possible difficulties with infinitely often adaptation.

Page Thumbnails

  • Thumbnail: Page 
261
    261
  • Thumbnail: Page 
262
    262
  • Thumbnail: Page 
263
    263
  • Thumbnail: Page 
264
    264
  • Thumbnail: Page 
265
    265
  • Thumbnail: Page 
266
    266
  • Thumbnail: Page 
267
    267
  • Thumbnail: Page 
268
    268
  • Thumbnail: Page 
269
    269
  • Thumbnail: Page 
270
    270
  • Thumbnail: Page 
271
    271
  • Thumbnail: Page 
272
    272
  • Thumbnail: Page 
273
    273
  • Thumbnail: Page 
274
    274
  • Thumbnail: Page 
275
    275
  • Thumbnail: Page 
276
    276