Essec\Faculty\Model\Contribution {#2216 ▼
#_index: "academ_contributions"
#_id: "12516"
#_source: array:26 [
"id" => "12516"
"slug" => "12516-unbiased-markov-chain-monte-carlo-methods-with-couplings"
"yearMonth" => "2020-07"
"year" => "2020"
"title" => "Unbiased Markov chain Monte Carlo methods with couplings"
"description" => "JACOB, P., O’LEARY, J. et ATCHADÉ, Y.F. (2020). Unbiased Markov chain Monte Carlo methods with couplings. <i>Journal of the Royal Statistical Society: Series B (Statistical Methodology)</i>, 82(3), pp. 543-600. 
JACOB, P., O’LEARY, J. et ATCHADÉ, Y.F. (2020). Unbiased Markov chain Monte Carlo methods with coupl "
"authors" => array:3 [
0 => array:3 [
"name" => "JACOB Pierre"
"bid" => "B00795650"
"slug" => "jacob-pierre"
]
1 => array:1 [
"name" => "O’LEARY John"
]
2 => array:1 [
"name" => "ATCHADÉ Yves F."
]
]
"ouvrage" => ""
"keywords" => array:5 [
0 => "Coupling estimation"
1 => "Markov chain"
2 => "Monte Carlo methods"
3 => "Parallel computing"
4 => "Unbiased"
]
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://rss.onlinelibrary.wiley.com/doi/10.1111/rssb.12336#"
"publicationInfo" => array:3 [
"pages" => "543-600"
"volume" => "82"
"number" => "3"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "Markov chain Monte Carlo (MCMC) methods provide consistent approximations of integrals as the number of iterations goes to ∞. MCMC estimators are generally biased after any fixed number of iterations. We propose to remove this bias by using couplings of Markov chains together with a telescopic sum argument of Glynn and Rhee. The resulting unbiased estimators can be computed independently in parallel. We discuss practical couplings for popular MCMC algorithms. We establish the theoretical validity of the estimators proposed and study their efficiency relative to the underlying MCMC algorithms. Finally, we illustrate the performance and limitations of the method on toy examples, on an Ising model around its critical temperature, on a high dimensional variable-selection problem, and on an approximation of the cut distribution arising in Bayesian inference for models made of multiple modules. Markov chain Monte Carlo (MCMC) methods provide consistent approximations of integrals as the number "
"en" => "Markov chain Monte Carlo (MCMC) methods provide consistent approximations of integrals as the number of iterations goes to ∞. MCMC estimators are generally biased after any fixed number of iterations. We propose to remove this bias by using couplings of Markov chains together with a telescopic sum argument of Glynn and Rhee. The resulting unbiased estimators can be computed independently in parallel. We discuss practical couplings for popular MCMC algorithms. We establish the theoretical validity of the estimators proposed and study their efficiency relative to the underlying MCMC algorithms. Finally, we illustrate the performance and limitations of the method on toy examples, on an Ising model around its critical temperature, on a high dimensional variable-selection problem, and on an approximation of the cut distribution arising in Bayesian inference for models made of multiple modules. Markov chain Monte Carlo (MCMC) methods provide consistent approximations of integrals as the number "
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-03-18T00:21:44.000Z"
"docTitle" => "Unbiased Markov chain Monte Carlo methods with couplings"
"docSurtitle" => "Journal articles"
"authorNames" => "<a href="/cv/jacob-pierre">JACOB Pierre</a>, O’LEARY John, ATCHADÉ Yves F."
"docDescription" => "<span class="document-property-authors">JACOB Pierre, O’LEARY John, ATCHADÉ Yves F.</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2020</span> <span class="document-property-authors">JACOB Pierre, O’LEARY John, ATCHADÉ Yves F.</span><br><span "
"keywordList" => "<a href="#">Coupling estimation</a>, <a href="#">Markov chain</a>, <a href="#">Monte Carlo methods</a>, <a href="#">Parallel computing</a>, <a href="#">Unbiased</a> <a href="#">Coupling estimation</a>, <a href="#">Markov chain</a>, <a href="#">Monte Carlo methods</ "
"docPreview" => "<b>Unbiased Markov chain Monte Carlo methods with couplings</b><br><span>2020-07 | Journal articles </span> <b>Unbiased Markov chain Monte Carlo methods with couplings</b><br><span>2020-07 | Journal articles "
"docType" => "research"
"publicationLink" => "<a href="https://rss.onlinelibrary.wiley.com/doi/10.1111/rssb.12336#" target="_blank">Unbiased Markov chain Monte Carlo methods with couplings</a> <a href="https://rss.onlinelibrary.wiley.com/doi/10.1111/rssb.12336#" target="_blank">Unbiased Marko "
]
+lang: "en"
+"_type": "_doc"
+"_score": 8.586569
+"parent": null
}
