Essec\Faculty\Model\Contribution {#2203
  #_index: "academ_contributions"
  #_id: "4453"
  #_source: array:26 [
    "id" => "4453"
    "slug" => "clustering-time-series-with-nonlinear-dynamics-a-bayesian-non-parametric-and-particle-based-approach"
    "yearMonth" => "2019-04"
    "year" => "2019"
    "title" => "Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach"
    "description" => "LIN, A., ZHANG, Y., HENG, J., ALLSOP, S.A., TYE, K.M. et JACOB, P. (2019). Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach. Dans: <i>Proceedings of Machine Learning Research</i>. "
    "authors" => array:6 [
      0 => array:3 [
        "name" => "HENG Jeremy"
        "bid" => "B00760223"
        "slug" => "heng-jeremy"
      ]
      1 => array:3 [
        "name" => "JACOB Pierre"
        "bid" => "B00795650"
        "slug" => "jacob-pierre"
      ]
      2 => array:1 [
        "name" => "LIN A"
      ]
      3 => array:1 [
        "name" => "ZHANG Y."
      ]
      4 => array:1 [
        "name" => "ALLSOP S. A."
      ]
      5 => array:1 [
        "name" => "TYE K. M."
      ]
    ]
    "ouvrage" => "Proceedings of Machine Learning Research"
    "keywords" => []
    "updatedAt" => "2021-09-24 10:33:27"
    "publicationUrl" => null
    "publicationInfo" => array:3 [
      "pages" => null
      "volume" => "89"
      "number" => null
    ]
    "type" => array:2 [
      "fr" => "Actes d'une conférence"
      "en" => "Conference Proceedings"
    ]
    "support_type" => array:2 [
      "fr" => null
      "en" => null
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "We propose a general statistical framework for clustering multiple time series that exhibit nonlinear dynamics into an a-priori-unknown number of sub-groups. Our motivation comes from neuroscience, where an important problem is to identify, within a large assembly of neurons, subsets that respond similarly to a stimulus or contingency. Upon modeling the multiple time series as the output of a Dirichlet process mixture of nonlinear state-space models, we derive a Metropolis-within-Gibbs algorithm for full Bayesian inference that alternates between sampling cluster assignments and sampling parameter values that form the basis of the clustering. The Metropolis step employs recent innovations in particle-based methods. We apply the framework to clustering time series acquired from the prefrontal cortex of mice in an experiment designed to characterize the neural underpinnings of fear."
      "en" => "We propose a general statistical framework for clustering multiple time series that exhibit nonlinear dynamics into an a-priori-unknown number of sub-groups. Our motivation comes from neuroscience, where an important problem is to identify, within a large assembly of neurons, subsets that respond similarly to a stimulus or contingency. Upon modeling the multiple time series as the output of a Dirichlet process mixture of nonlinear state-space models, we derive a Metropolis-within-Gibbs algorithm for full Bayesian inference that alternates between sampling cluster assignments and sampling parameter values that form the basis of the clustering. The Metropolis step employs recent innovations in particle-based methods. We apply the framework to clustering time series acquired from the prefrontal cortex of mice in an experiment designed to characterize the neural underpinnings of fear."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
      "en" => "Information Systems, Decision Sciences and Statistics"
    ]
    "indexedAt" => "2024-04-23T10:22:01.000Z"
    "docTitle" => "Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach"
    "docSurtitle" => "Actes d'une conférence"
    "authorNames" => "<a href="/cv/heng-jeremy">HENG Jeremy</a>, <a href="/cv/jacob-pierre">JACOB Pierre</a>, LIN A, ZHANG Y., ALLSOP S. A., TYE K. M."
    "docDescription" => "<span class="document-property-authors">HENG Jeremy, JACOB Pierre, LIN A, ZHANG Y., ALLSOP S. A., TYE K. M.</span><br><span class="document-property-authors_fields">Systèmes d’Information, Sciences de la Décision et Statistiques</span> | <span class="document-property-year">2019</span>"
    "keywordList" => ""
    "docPreview" => "<b>Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach</b><br><span>2019-04 | Actes d'une conférence </span>"
    "docType" => "research"
    "publicationLink" => "<a href="#" target="_blank">Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 8.410244
  +"parent": null
}