On the efficiency of adaptive MCMC algorithms

Christophe Andrieu*, Yves F. Atchadé

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

23 Citations (Scopus)


We study a class of adaptive Markov Chain Monte Carlo (MCMC) processes which aim at behaving as an "optimal" target process via a learning procedure. We show, under appropriate conditions, that the adaptive MCMC chain and the "optimal" (nonadaptive) MCMC process share many asymptotic properties. The special case of adaptive MCMC algorithms governed by stochastic approximation is considered in details and we apply our results to the adaptive Metropolis algorithm of Haario et al. (2001).

Original languageEnglish
Pages (from-to)336-349
Number of pages14
JournalElectronic Communications in Probability
Publication statusPublished - 12 Oct 2007


  • Adaptive Markov chains
  • Coupling
  • Markov chain Monte Carlo
  • Metropolis algorithm
  • Rate of convergence
  • Stochastic approximation

Fingerprint Dive into the research topics of 'On the efficiency of adaptive MCMC algorithms'. Together they form a unique fingerprint.

Cite this