Essec\Faculty\Model\Contribution {#2233
  #_index: "academ_contributions"
  #_id: "12534"
  #_source: array:26 [
    "id" => "12534"
    "slug" => "adaptive-tuning-of-hamiltonian-monte-carlo-within-sequential-monte-carlo"
    "yearMonth" => "2021-01"
    "year" => "2021"
    "title" => "Adaptive Tuning of Hamiltonian Monte Carlo Within Sequential Monte Carlo"
    "description" => "BUCHHOLZ, A., CHOPIN, N. et JACOB, P. (2021). Adaptive Tuning of Hamiltonian Monte Carlo Within Sequential Monte Carlo. <i>Bayesian Analysis</i>, 16(3), pp. 745-777."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "JACOB Pierre"
        "bid" => "B00795650"
        "slug" => "jacob-pierre"
      ]
      1 => array:1 [
        "name" => "BUCHHOLZ Alexander"
      ]
      2 => array:1 [
        "name" => "CHOPIN Nicolas"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:2 [
      0 => "Hamiltonian Monte Carlo"
      1 => "sequential Monte Carlo"
    ]
    "updatedAt" => "2021-09-24 10:33:27"
    "publicationUrl" => null
    "publicationInfo" => array:3 [
      "pages" => "745-777"
      "volume" => "16"
      "number" => "3"
    ]
    "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" => "Sequential Monte Carlo (SMC) samplers are an alternative to MCMC for Bayesian computation. However, their performance depends strongly on the Markov kernels used to rejuvenate particles. We discuss how to calibrate automatically (using the current particles) Hamiltonian Monte Carlo kernels within SMC. To do so, we build upon the adaptive SMC approach of Fearnhead and Taylor (2013), and we also suggest alternative methods. We illustrate the advantages of using HMC kernels within an SMC sampler via an extensive numerical study."
      "en" => "Sequential Monte Carlo (SMC) samplers are an alternative to MCMC for Bayesian computation. However, their performance depends strongly on the Markov kernels used to rejuvenate particles. We discuss how to calibrate automatically (using the current particles) Hamiltonian Monte Carlo kernels within SMC. To do so, we build upon the adaptive SMC approach of Fearnhead and Taylor (2013), and we also suggest alternative methods. We illustrate the advantages of using HMC kernels within an SMC sampler via an extensive numerical study."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-12-22T08:21:46.000Z"
    "docTitle" => "Adaptive Tuning of Hamiltonian Monte Carlo Within Sequential Monte Carlo"
    "docSurtitle" => "Articles"
    "authorNames" => "<a href="/cv/jacob-pierre">JACOB Pierre</a>, BUCHHOLZ Alexander, CHOPIN Nicolas"
    "docDescription" => "<span class="document-property-authors">JACOB Pierre, BUCHHOLZ Alexander, CHOPIN Nicolas</span><br><span class="document-property-authors_fields">Systèmes d'Information, Data Analytics et Opérations</span> | <span class="document-property-year">2021</span>"
    "keywordList" => "<a href="#">Hamiltonian Monte Carlo</a>, <a href="#">sequential Monte Carlo</a>"
    "docPreview" => "<b>Adaptive Tuning of Hamiltonian Monte Carlo Within Sequential Monte Carlo</b><br><span>2021-01 | Articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="#" target="_blank">Adaptive Tuning of Hamiltonian Monte Carlo Within Sequential Monte Carlo</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 8.831362
  +"parent": null
}