EconPapers    
Economics at your fingertips  
 

Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms

G. O. Roberts and A. F. M. Smith

Stochastic Processes and their Applications, 1994, vol. 49, issue 2, 207-216

Abstract: Markov chain Monte Carlo (MCMC) simulation methods are being used increasingly in statistical computation to explore and estimate features of likelihood surfaces and Bayesian posterior distributions. This paper presents simple conditions which ensure the convergence of two widely used versions of MCMC, the Gibbs sampler and Metropolis-Hastings algorithms.

Keywords: Markov; chain; Monte; Carlo; Gibbs; sampler; Metropolis-Hastings; algorithm; statistical; computation; ergodicity; lower; semicontinuity (search for similar items in EconPapers)
Date: 1994
References: Add references at CitEc
Citations View citations in EconPapers (23) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0304-4149(94)90134-1
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:spapps:v:49:y:1994:i:2:p:207-216

Ordering information: This journal article can be ordered from
http://http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Stochastic Processes and their Applications is currently edited by T. Mikosch

More articles in Stochastic Processes and their Applications from Elsevier
Series data maintained by Dana Niculescu ().

 
Page updated 2017-09-29
Handle: RePEc:eee:spapps:v:49:y:1994:i:2:p:207-216