Essec\Faculty\Model\Contribution {#2205
  #_index: "academ_contributions"
  #_id: "14737"
  #_source: array:26 [
    "id" => "14737"
    "slug" => "optimistic-estimation-of-convergence-in-markov-chains-with-the-average-mixing-time"
    "yearMonth" => "2024-07"
    "year" => "2024"
    "title" => "Optimistic Estimation of Convergence in Markov Chains with the Average Mixing Time"
    "description" => "WOLFER, G. et ALQUIER, P. (2024). Optimistic Estimation of Convergence in Markov Chains with the Average Mixing Time. Dans: International Conference on Scientific Computation and Differential Equations. Singapore."
    "authors" => array:2 [
      0 => array:3 [
        "name" => "ALQUIER Pierre"
        "bid" => "B00809923"
        "slug" => "alquier-pierre"
      ]
      1 => array:1 [
        "name" => "WOLFER Geoffrey"
      ]
    ]
    "ouvrage" => "International Conference on Scientific Computation and Differential Equations"
    "keywords" => array:1 [
      0 => "Markov chains"
    ]
    "updatedAt" => "2024-07-15 12:32:43"
    "publicationUrl" => null
    "publicationInfo" => array:3 [
      "pages" => ""
      "volume" => ""
      "number" => ""
    ]
    "type" => array:2 [
      "fr" => "Communications dans une conférence"
      "en" => "Presentations at an Academic or Professional conference"
    ]
    "support_type" => array:2 [
      "fr" => null
      "en" => null
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "The convergence rate of a Markov chain to its stationary distribution is typically assessed using the concept of total variation mixing time. However, this worst-case measure often yields pessimistic estimates and is challenging to infer from observations. In this paper, we advocate for the use of the average-mixing time as a more optimistic and demonstrably easier-to-estimate alternative. We further illustrate its applicability across a range of settings, from two-point to countable spaces, and discuss some practical implications."
      "en" => "The convergence rate of a Markov chain to its stationary distribution is typically assessed using the concept of total variation mixing time. However, this worst-case measure often yields pessimistic estimates and is challenging to infer from observations. In this paper, we advocate for the use of the average-mixing time as a more optimistic and demonstrably easier-to-estimate alternative. We further illustrate its applicability across a range of settings, from two-point to countable spaces, and discuss some practical implications."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
      "en" => "Information Systems, Decision Sciences and Statistics"
    ]
    "indexedAt" => "2024-07-16T02:22:05.000Z"
    "docTitle" => "Optimistic Estimation of Convergence in Markov Chains with the Average Mixing Time"
    "docSurtitle" => "Communications dans une conférence"
    "authorNames" => "<a href="/cv/alquier-pierre">ALQUIER Pierre</a>, WOLFER Geoffrey"
    "docDescription" => "<span class="document-property-authors">ALQUIER Pierre, WOLFER Geoffrey</span><br><span class="document-property-authors_fields">Systèmes d’Information, Sciences de la Décision et Statistiques</span> | <span class="document-property-year">2024</span>"
    "keywordList" => "<a href="#">Markov chains</a>"
    "docPreview" => "<b>Optimistic Estimation of Convergence in Markov Chains with the Average Mixing Time</b><br><span>2024-07 | Communications dans une conférence </span>"
    "docType" => "research"
    "publicationLink" => "<a href="#" target="_blank">Optimistic Estimation of Convergence in Markov Chains with the Average Mixing Time</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 8.74219
  +"parent": null
}