A tutorial on adaptive MCMC

C Andrieu, J Thoms

Research output: Contribution to journalArticle (Academic Journal)

333 Citations (Scopus)

Abstract

We review adaptive Markov chain Monte Carlo algorithms (MCMC) as a mean to optimise their performance. Using simple toy examples we review their theoretical underpinnings, and in particular show why adaptive MCMC algorithms might fail when some fundamental properties are not satisfied. This leads to guidelines concerning the design of correct algorithms. We then review criteria and the useful framework of stochastic approximation, which allows one to systematically optimise generally used criteria, but also analyse the properties of adaptive MCMC algorithms. We then propose a series of novel adaptive algorithms which prove to be robust and reliable in practice. These algorithms are applied to artificial and high dimensional scenarios, but also to the classic mine disaster dataset inference problem.
Original languageEnglish
Pages (from-to)343 - 373
Number of pages31
JournalStatistics and Computing
Volume18, issue 4
DOIs
Publication statusPublished - Dec 2008

Bibliographical note

Publisher: Springer Netherlands

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