Essec\Faculty\Model\Contribution {#6196
  #_index: "academ_contributions"
  #_id: "12519"
  #_source: array:26 [
    "id" => "12519"
    "slug" => "smoothing-with-couplings-of-conditional-particle-filters"
    "yearMonth" => "2020-04"
    "year" => "2020"
    "title" => "Smoothing With Couplings of Conditional Particle Filters"
    "description" => "JACOB, P., LINDSTEN, F. et SCHÖN, T.B. (2020). Smoothing With Couplings of Conditional Particle Filters. <i>Journal of the American Statistical Association</i>, 115(530), pp. 721-729."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "JACOB Pierre"
        "bid" => "B00795650"
        "slug" => "jacob-pierre"
      ]
      1 => array:1 [
        "name" => "LINDSTEN Fredrik"
      ]
      2 => array:1 [
        "name" => "SCHÖN Thomas B."
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:5 [
      0 => "Couplings"
      1 => "Debiasing techniques"
      2 => "Parallel computation"
      3 => "Particle filtering"
      4 => "Particle smoothing"
    ]
    "updatedAt" => "2021-09-24 10:33:27"
    "publicationUrl" => "https://www.tandfonline.com/doi/full/10.1080/01621459.2018.1548856"
    "publicationInfo" => array:3 [
      "pages" => "721-729"
      "volume" => "115"
      "number" => "530"
    ]
    "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" => "In state–space models, smoothing refers to the task of estimating a latent stochastic process given noisy measurements related to the process. We propose an unbiased estimator of smoothing expectations. The lack-of-bias property has methodological benefits: independent estimators can be generated in parallel, and CI can be constructed from the central limit theorem to quantify the approximation error. To design unbiased estimators, we combine a generic debiasing technique for Markov chains, with a Markov chain Monte Carlo algorithm for smoothing. The resulting procedure is widely applicable and we show in numerical experiments that the removal of the bias comes at a manageable increase in variance. We establish the validity of the proposed estimators under mild assumptions. Numerical experiments are provided on toy models, including a setting of highly informative observations, and for a realistic Lotka–Volterra model with an intractable transition density. Supplementary materials for this article are available online."
      "en" => "In state–space models, smoothing refers to the task of estimating a latent stochastic process given noisy measurements related to the process. We propose an unbiased estimator of smoothing expectations. The lack-of-bias property has methodological benefits: independent estimators can be generated in parallel, and CI can be constructed from the central limit theorem to quantify the approximation error. To design unbiased estimators, we combine a generic debiasing technique for Markov chains, with a Markov chain Monte Carlo algorithm for smoothing. The resulting procedure is widely applicable and we show in numerical experiments that the removal of the bias comes at a manageable increase in variance. We establish the validity of the proposed estimators under mild assumptions. Numerical experiments are provided on toy models, including a setting of highly informative observations, and for a realistic Lotka–Volterra model with an intractable transition density. Supplementary materials for this article are available online."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
      "en" => "Information Systems, Decision Sciences and Statistics"
    ]
    "indexedAt" => "2024-04-20T05:21:45.000Z"
    "docTitle" => "Smoothing With Couplings of Conditional Particle Filters"
    "docSurtitle" => "Articles"
    "authorNames" => "<a href="/cv/jacob-pierre">JACOB Pierre</a>, LINDSTEN Fredrik, SCHÖN Thomas B."
    "docDescription" => "<span class="document-property-authors">JACOB Pierre, LINDSTEN Fredrik, SCHÖN Thomas B.</span><br><span class="document-property-authors_fields">Systèmes d’Information, Sciences de la Décision et Statistiques</span> | <span class="document-property-year">2020</span>"
    "keywordList" => "<a href="#">Couplings</a>, <a href="#">Debiasing techniques</a>, <a href="#">Parallel computation</a>, <a href="#">Particle filtering</a>, <a href="#">Particle smoothing</a>"
    "docPreview" => "<b>Smoothing With Couplings of Conditional Particle Filters</b><br><span>2020-04 | Articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://www.tandfonline.com/doi/full/10.1080/01621459.2018.1548856" target="_blank">Smoothing With Couplings of Conditional Particle Filters</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 9.006263
  +"parent": null
}