Essec\Faculty\Model\Contribution {#6196`
#_index: "academ_contributions"
#_id: "13049"
#_source: array:26 [``
"id" => "13049"
"slug" => "an-invitation-to-sequential-monte-carlo-samplers"
"yearMonth" => "2022-07"
"year" => "2022"
"title" => "An invitation to sequential Monte Carlo samplers"
"description" => "DAI, C., HENG, J., JACOB, P. et WHITELEY, N. (2022). An invitation to sequential Monte Carlo samplers. <i>Journal of the American Statistical Association</i>, 117(539), pp. 1587-1600."
"authors" => array:4 [``
0 => array:3 [``
"name" => "HENG Jeremy"
"bid" => "B00760223"
"slug" => "heng-jeremy"
`]
1 => array:3 [`
"name" => "JACOB Pierre"
"bid" => "B00795650"
"slug" => "jacob-pierre"
`]
2 => array:1 [`
"name" => "DAI Chenguang"
`]
3 => array:1 [`
"name" => "WHITELEY Nick"
`]
]
"ouvrage" => ""
"keywords" => array:4 [`
0 => "Monte Carlo methods"
1 => "sequential inference"
2 => "normalizing constant"
3 => "interacting particle systems"
`]
"updatedAt" => "2023-07-18 16:16:04"
"publicationUrl" => "https://www.tandfonline.com/doi/full/10.1080/01621459.2022.2087659"
"publicationInfo" => array:3 [`
"pages" => "1587-1600"
"volume" => "117"
"number" => "539"
`]
"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" => "Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to approximate distributions of interest and their normalizing constants. These samplers originate from particle filtering for state space models and have become general and scalable sampling techniques. This article describes sequential Monte Carlo samplers and their possible implementations, arguing that they remain under-used in statistics, despite their ability to perform sequential inference and to leverage parallel processing resources among other potential benefits."
"en" => "Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to approximate distributions of interest and their normalizing constants. These samplers originate from particle filtering for state space models and have become general and scalable sampling techniques. This article describes sequential Monte Carlo samplers and their possible implementations, arguing that they remain under-used in statistics, despite their ability to perform sequential inference and to leverage parallel processing resources among other potential benefits."
`]
"authors_fields" => array:2 [`
"fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
"en" => "Information Systems, Decision Sciences and Statistics"
`]
"indexedAt" => "2024-02-27T07:21:46.000Z"
"docTitle" => "An invitation to sequential Monte Carlo samplers"
"docSurtitle" => "Articles"
"authorNames" => "<a href="/cv/heng-jeremy">HENG Jeremy</a>, <a href="/cv/jacob-pierre">JACOB Pierre</a>, DAI Chenguang, WHITELEY Nick"
"docDescription" => "<span class="document-property-authors">HENG Jeremy, JACOB Pierre, DAI Chenguang, WHITELEY Nick</span><br><span class="document-property-authors_fields">Systèmes d’Information, Sciences de la Décision et Statistiques</span> | <span class="document-property-year">2022</span>"
"keywordList" => "<a href="#">Monte Carlo methods</a>, <a href="#">sequential inference</a>, <a href="#">normalizing constant</a>, <a href="#">interacting particle systems</a>"
"docPreview" => "<b>An invitation to sequential Monte Carlo samplers</b><br><span>2022-07 | Articles </span>"
"docType" => "research"
"publicationLink" => "<a href="https://www.tandfonline.com/doi/full/10.1080/01621459.2022.2087659" target="_blank">An invitation to sequential Monte Carlo samplers</a>"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 8.5994
+"parent": null
}