Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "12111"
  #_source: array:26 [
    "id" => "12111"
    "slug" => "gibbs-flow-for-approximate-transport-with-applications-to-bayesian-computation"
    "yearMonth" => "2021-02"
    "year" => "2021"
    "title" => "Gibbs flow for approximate transport with applications to Bayesian computation"
    "description" => "HENG, J., DOUCET, A. et POKERN, Y. (2021). Gibbs flow for approximate transport with applications to Bayesian computation. <i>Journal of the Royal Statistical Society: Series B (Statistical Methodology)</i>, 83(1), pp. 157-187."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "HENG Jeremy"
        "bid" => "B00760223"
        "slug" => "heng-jeremy"
      ]
      1 => array:1 [
        "name" => "DOUCET Arnaud"
      ]
      2 => array:1 [
        "name" => "POKERN Yvo"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:5 [
      0 => "Markoc chain"
      1 => "Monte Carlo"
      2 => "mass transport"
      3 => "normalising constants"
      4 => "path sampling"
    ]
    "updatedAt" => "2024-10-31 13:51:19"
    "publicationUrl" => "https://doi.org/10.1111/rssb.12404"
    "publicationInfo" => array:3 [
      "pages" => "157-187"
      "volume" => "83"
      "number" => "1"
    ]
    "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" => "We show here how to build a tractable approximation of a novel transport map. Even when this ordinary differential equation is time‐discretised and the full conditional distributions are numerically approximated, the resulting distribution of mapped samples can be efficiently evaluated and used as a proposal within sequential Monte Carlo samplers. We demonstrate significant gains over state‐of‐the‐art sequential Monte Carlo samplers at a fixed computational complexity on a variety of applications."
      "en" => "We show here how to build a tractable approximation of a novel transport map. Even when this ordinary differential equation is time‐discretised and the full conditional distributions are numerically approximated, the resulting distribution of mapped samples can be efficiently evaluated and used as a proposal within sequential Monte Carlo samplers. We demonstrate significant gains over state‐of‐the‐art sequential Monte Carlo samplers at a fixed computational complexity on a variety of applications."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-11-21T11:21:49.000Z"
    "docTitle" => "Gibbs flow for approximate transport with applications to Bayesian computation"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/heng-jeremy">HENG Jeremy</a>, DOUCET Arnaud, POKERN Yvo"
    "docDescription" => "<span class="document-property-authors">HENG Jeremy, DOUCET Arnaud, POKERN Yvo</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2021</span>"
    "keywordList" => "<a href="#">Markoc chain</a>, <a href="#">Monte Carlo</a>, <a href="#">mass transport</a>, <a href="#">normalising constants</a>, <a href="#">path sampling</a>"
    "docPreview" => "<b>Gibbs flow for approximate transport with applications to Bayesian computation</b><br><span>2021-02 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1111/rssb.12404" target="_blank">Gibbs flow for approximate transport with applications to Bayesian computation</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.442641
  +"parent": null
}