Essec\Faculty\Model\Contribution {#2188
  #_index: "academ_contributions"
  #_id: "13866"
  #_source: array:26 [
    "id" => "13866"
    "slug" => "consistency-of-variational-bayes-inference-for-estimation-and-model-selection-in-mixtures"
    "yearMonth" => "2018-09"
    "year" => "2018"
    "title" => "Consistency of variational Bayes inference for estimation and model selection in mixtures"
    "description" => "CHERIEF-ABDELLATIF, B.E. et ALQUIER, P. (2018). Consistency of variational Bayes inference for estimation and model selection in mixtures. <i>The Electronic Journal of Statistics</i>, 12(2), pp. 2995-3035."
    "authors" => array:2 [
      0 => array:3 [
        "name" => "ALQUIER Pierre"
        "bid" => "B00809923"
        "slug" => "alquier-pierre"
      ]
      1 => array:1 [
        "name" => "CHERIEF-ABDELLATIF Badr-Eddine"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:4 [
      0 => "Mixture models"
      1 => "frequentist evaluation of Bayesian methods"
      2 => "variational approximations"
      3 => "model selection"
    ]
    "updatedAt" => "2023-03-17 01:00:45"
    "publicationUrl" => "https://doi.org/10.1214/18-EJS1475"
    "publicationInfo" => array:3 [
      "pages" => "2995-3035"
      "volume" => "12"
      "number" => "2"
    ]
    "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" => "Mixture models are widely used in Bayesian statistics and machine learning, in particular in computational biology, natural language processing and many other fields. Variational inference, a technique for approximating intractable posteriors thanks to optimization algorithms, is extremely popular in practice when dealing with complex models such as mixtures. The contribution of this paper is two-fold. First, we study the concentration of variational approximations of posteriors, which is still an open problem for general mixtures, and we derive consistency and rates of convergence. We also tackle the problem of model selection for the number of components: we study the approach already used in practice, which consists in maximizing a numerical criterion (the Evidence Lower Bound). We prove that this strategy indeed leads to strong oracle inequalities. We illustrate our theoretical results by applications to Gaussian and multinomial mixtures."
      "en" => "Mixture models are widely used in Bayesian statistics and machine learning, in particular in computational biology, natural language processing and many other fields. Variational inference, a technique for approximating intractable posteriors thanks to optimization algorithms, is extremely popular in practice when dealing with complex models such as mixtures. The contribution of this paper is two-fold. First, we study the concentration of variational approximations of posteriors, which is still an open problem for general mixtures, and we derive consistency and rates of convergence. We also tackle the problem of model selection for the number of components: we study the approach already used in practice, which consists in maximizing a numerical criterion (the Evidence Lower Bound). We prove that this strategy indeed leads to strong oracle inequalities. We illustrate our theoretical results by applications to Gaussian and multinomial mixtures."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
      "en" => "Information Systems, Decision Sciences and Statistics"
    ]
    "indexedAt" => "2024-04-24T04:22:08.000Z"
    "docTitle" => "Consistency of variational Bayes inference for estimation and model selection in mixtures"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/alquier-pierre">ALQUIER Pierre</a>, CHERIEF-ABDELLATIF Badr-Eddine"
    "docDescription" => "<span class="document-property-authors">ALQUIER Pierre, CHERIEF-ABDELLATIF Badr-Eddine</span><br><span class="document-property-authors_fields">Information Systems, Decision Sciences and Statistics</span> | <span class="document-property-year">2018</span>"
    "keywordList" => "<a href="#">Mixture models</a>, <a href="#">frequentist evaluation of Bayesian methods</a>, <a href="#">variational approximations</a>, <a href="#">model selection</a>"
    "docPreview" => "<b>Consistency of variational Bayes inference for estimation and model selection in mixtures</b><br><span>2018-09 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1214/18-EJS1475" target="_blank">Consistency of variational Bayes inference for estimation and model selection in mixtures</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.514871
  +"parent": null
}