Essec\Faculty\Model\Contribution {#2190`
#_index: "academ_contributions"
#_id: "13900"
#_source: array:26 [``
"id" => "13900"
"slug" => "noisy-monte-carlo-convergence-of-markov-chains-with-approximate-transition-kernels"
"yearMonth" => "2016-01"
"year" => "2016"
"title" => "Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels"
"description" => "ALQUIER, P., FRIEL, N., EVERITT, R. et BOLAND, A. (2016). Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels. <i>Statistics and Computing</i>, 26(1-2), pp. 29-47."
"authors" => array:4 [``
0 => array:3 [``
"name" => "ALQUIER Pierre"
"bid" => "B00809923"
"slug" => "alquier-pierre"
`]
1 => array:1 [`
"name" => "FRIEL N."
`]
2 => array:1 [`
"name" => "EVERITT R."
`]
3 => array:1 [`
"name" => "BOLAND A."
`]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2023-03-22 09:58:38"
"publicationUrl" => "https://link.springer.com/article/10.1007/s11222-014-9521-x"
"publicationInfo" => array:3 [`
"pages" => "29-47"
"volume" => "26"
"number" => "1-2"
`]
"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" => "Monte Carlo algorithms often aim to draw from a distribution π by simulating a Markov chain with transition kernel P such that π is invariant under P. However, there are many situations for which it is impractical or impossible to draw from the transition kernel P. For instance, this is the case with massive datasets, where is it prohibitively expensive to calculate the likelihood and is also the case for intractable likelihood models arising from, for example, Gibbs random fields, such as those found in spatial statistics and network analysis."
"en" => "Monte Carlo algorithms often aim to draw from a distribution π by simulating a Markov chain with transition kernel P such that π is invariant under P. However, there are many situations for which it is impractical or impossible to draw from the transition kernel P. For instance, this is the case with massive datasets, where is it prohibitively expensive to calculate the likelihood and is also the case for intractable likelihood models arising from, for example, Gibbs random fields, such as those found in spatial statistics and network analysis."
`]
"authors_fields" => array:2 [`
"fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
"en" => "Information Systems, Decision Sciences and Statistics"
`]
"indexedAt" => "2024-03-04T01:21:51.000Z"
"docTitle" => "Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels"
"docSurtitle" => "Journal articles"
"authorNames" => "<a href="/cv/alquier-pierre">ALQUIER Pierre</a>, FRIEL N., EVERITT R., BOLAND A."
"docDescription" => "<span class="document-property-authors">ALQUIER Pierre, FRIEL N., EVERITT R., BOLAND A.</span><br><span class="document-property-authors_fields">Information Systems, Decision Sciences and Statistics</span> | <span class="document-property-year">2016</span>"
"keywordList" => ""
"docPreview" => "<b>Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels</b><br><span>2016-01 | Journal articles </span>"
"docType" => "research"
"publicationLink" => "<a href="https://link.springer.com/article/10.1007/s11222-014-9521-x" target="_blank">Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels</a>"
]
+lang: "en"
+"_type": "_doc"
+"_score": 8.453102
+"parent": null
}