Essec\Faculty\Model\Contribution {#2237
  #_index: "academ_contributions"
  #_id: "16508"
  #_source: array:26 [
    "id" => 16508
    "slug" => "16508-benchmark-of-likelihood-free-inference-methods-based-on-neural-and-optimal-transport-approaches"
    "yearMonth" => "2026-05"
    "year" => 2026
    "title" => "Benchmark of Likelihood-Free Inference Methods based on Neural and Optimal Transport Approaches"
    "description" => "AKA, S., KRATZ, M. et NAVEAU, P. (2026). <i>Benchmark of Likelihood-Free Inference Methods based on Neural and Optimal Transport Approaches</i>. hal-05639283, ESSEC Business School."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "KRATZ Marie"
        "bid" => "B00072305"
        "slug" => "kratz-marie"
      ]
      1 => array:1 [
        "name" => "AKA Samira"
      ]
      2 => array:1 [
        "name" => "NAVEAU Philippe"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:5 [
      0 => "Simulation-based inference"
      1 => "robust point estimation"
      2 => "model misspecification"
      3 => """
        \n
        heavy-tailed distributions
        """
      4 => "discrete data"
    ]
    "updatedAt" => "2026-06-04 15:01:23"
    "publicationUrl" => "https://hal.science/hal-05639283v1"
    "publicationInfo" => array:3 [
      "pages" => ""
      "volume" => ""
      "number" => ""
    ]
    "type" => array:2 [
      "fr" => "Documents de travail"
      "en" => "Working Papers"
    ]
    "support_type" => array:2 [
      "fr" => "Cahier de Recherche"
      "en" => "Working Papers"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => """
        Simulation-based inference (SBI) has become an increasingly important framework for parameter estimation in models for which simulation is feasible, including cases where likelihood evaluation is unavailable or costly. While recent work has introduced benchmark frameworks to compare likelihood-free methods, these studies often do not account for structural features such as heavy-tails or discreteness.\n
        In this article, we investigate how the performance of likelihood-free inference\n
        methods depends on these structural properties. We consider four approaches: MLE, NBE, EOT and AW–NBE and evaluate them using simulations. This study highlights the importance of carefully selecting evaluation tools in the presence of extremes and discrete data.
        """
      "en" => """
        Simulation-based inference (SBI) has become an increasingly important framework for parameter estimation in models for which simulation is feasible, including cases where likelihood evaluation is unavailable or costly. While recent work has introduced benchmark frameworks to compare likelihood-free methods, these studies often do not account for structural features such as heavy-tails or discreteness.\n
        In this article, we investigate how the performance of likelihood-free inference\n
        methods depends on these structural properties. We consider four approaches: MLE, NBE, EOT and AW–NBE and evaluate them using simulations. This study highlights the importance of carefully selecting evaluation tools in the presence of extremes and discrete data.
        """
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2026-07-15T23:23:26.000Z"
    "docTitle" => "Benchmark of Likelihood-Free Inference Methods based on Neural and Optimal Transport Approaches"
    "docSurtitle" => "Documents de travail"
    "authorNames" => "<a href="/cv/kratz-marie">KRATZ Marie</a>, AKA Samira, NAVEAU Philippe"
    "docDescription" => "<span class="document-property-authors">KRATZ Marie, AKA Samira, NAVEAU Philippe</span><br><span class="document-property-authors_fields">Systèmes d'Information, Data Analytics et Opérations</span> | <span class="document-property-year">2026</span>"
    "keywordList" => """
      <a href="#">Simulation-based inference</a>, <a href="#">robust point estimation</a>, <a href="#">model misspecification</a>, <a href="#">\n
      heavy-tailed distributions</a>, <a href="#">discrete data</a>
      """
    "docPreview" => "<b>Benchmark of Likelihood-Free Inference Methods based on Neural and Optimal Transport Approaches</b><br><span>2026-05 | Documents de travail </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://hal.science/hal-05639283v1" target="_blank">Benchmark of Likelihood-Free Inference Methods based on Neural and Optimal Transport Approaches</a>"
  ]
  +lang: "fr"
  +"_score": 8.563695
  +"_ignored": array:2 [
    0 => "abstract.en.keyword"
    1 => "abstract.fr.keyword"
  ]
  +"parent": null
}