Resampling from the past to improve on MCMC algorithms
AbstractWe introduce the idea that resampling from past observations in a Markov Chain Monte Carlo sampler can fasten convergence. We prove that proper resampling from the past does not disturb the limit distribution of the algorithm. We illustrate the method with two examples. The first on a Bayesian analysis of stochastic volatility models and the other on Bayesian phylogeny reconstruction.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Département des sciences administratives, UQO in its series RePAd Working Paper Series with number LRSP-WP2.
Length: 28 pages
Date of creation: 07 Mar 2006
Date of revision:
Contact details of provider:
Postal: Pavillon Lucien Brault, 101 rue Saint Jean-Bosco, Gatineau (Québec) J8Y 3G5
Phone: (819) 595-3900
Fax: (819) 773-1747
Web page: http://www.repad.org/
More information through EDIRC
Monte Carlo methods; Resampling; Stochastic volatility models; Bayesian phylogeny reconstruction.;
Find related papers by JEL classification:
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
This paper has been announced in the following NEP Reports:
You can help add them by filling out this form.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christian Calmes).
If references are entirely missing, you can add them using this form.