Essec\Faculty\Model\Contribution {#2233
  #_index: "academ_contributions"
  #_id: "13884"
  #_source: array:26 [
    "id" => "13884"
    "slug" => "approximate-bayesian-inference"
    "yearMonth" => "2022-05"
    "year" => "2022"
    "title" => "Approximate Bayesian Inference"
    "description" => "ALQUIER, P. [Ed] (2022). <i>Approximate Bayesian Inference</i>. MDPI."
    "authors" => array:1 [
      0 => array:3 [
        "name" => "ALQUIER Pierre"
        "bid" => "B00809923"
        "slug" => "alquier-pierre"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:11 [
      0 => "Bayesian statistics"
      1 => "machine learning"
      2 => "variational approximations"
      3 => "PAC-Bayes"
      4 => "expectation-propagation"
      5 => "Markov chain Monte Carlo"
      6 => "Langevin Monte Carlo"
      7 => "sequential Monte Carlo"
      8 => "Laplace approximations"
      9 => "approximate Bayesian computation"
      10 => "Gibbs posterior"
    ]
    "updatedAt" => "2024-10-31 13:51:19"
    "publicationUrl" => "https://www.mdpi.com/books/book/5544"
    "publicationInfo" => array:3 [
      "pages" => null
      "volume" => null
      "number" => null
    ]
    "type" => array:2 [
      "fr" => "Direction d'ouvrage"
      "en" => "Book editor"
    ]
    "support_type" => array:2 [
      "fr" => "Editeur"
      "en" => "Publisher"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => """
        This book is a reprint of the Special Issue Approximate Bayesian Inference that was published in the open access journal Entropy (ISSN 1099-4300) (available at: https://www.mdpi.com/journal/entropy/special_issues/approx_Bayes_inference).\n
        The objective of this Special Issue is to provide the latest advances in approximate Monte Carlo methods and in approximations of the posterior: design of efficient algorithms, study of the statistical properties of these algorithms, and challenging applications.
        """
      "en" => """
        This book is a reprint of the Special Issue Approximate Bayesian Inference that was published in the open access journal Entropy (ISSN 1099-4300) (available at: https://www.mdpi.com/journal/entropy/special_issues/approx_Bayes_inference).\n
        The objective of this Special Issue is to provide the latest advances in approximate Monte Carlo methods and in approximations of the posterior: design of efficient algorithms, study of the statistical properties of these algorithms, and challenging applications.
        """
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-11-21T08:21:48.000Z"
    "docTitle" => "Approximate Bayesian Inference"
    "docSurtitle" => "Direction d'ouvrage"
    "authorNames" => "<a href="/cv/alquier-pierre">ALQUIER Pierre</a>"
    "docDescription" => "<span class="document-property-authors">ALQUIER Pierre</span><br><span class="document-property-authors_fields">Systèmes d'Information, Data Analytics et Opérations</span> | <span class="document-property-year">2022</span>"
    "keywordList" => "<a href="#">Bayesian statistics</a>, <a href="#">machine learning</a>, <a href="#">variational approximations</a>, <a href="#">PAC-Bayes</a>, <a href="#">expectation-propagation</a>, <a href="#">Markov chain Monte Carlo</a>, <a href="#">Langevin Monte Carlo</a>, <a href="#">sequential Monte Carlo</a>, <a href="#">Laplace approximations</a>, <a href="#">approximate Bayesian computation</a>, <a href="#">Gibbs posterior</a>"
    "docPreview" => "<b>Approximate Bayesian Inference</b><br><span>2022-05 | Direction d'ouvrage </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://www.mdpi.com/books/book/5544" target="_blank">Approximate Bayesian Inference</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 8.025807
  +"parent": null
}