Essec\Faculty\Model\Contribution {#2216 ▼
#_index: "academ_contributions"
#_id: "4453"
#_source: array:26 [
"id" => "4453"
"slug" => "4453-clustering-time-series-with-nonlinear-dynamics-a-bayesian-non-parametric-and-particle-based-approach 
4453-clustering-time-series-with-nonlinear-dynamics-a-bayesian-non-parametric-and-particle-based-app "
"yearMonth" => "2019-04"
"year" => "2019"
"title" => "Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approac "
"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>. LIN, A., ZHANG, Y., HENG, J., ALLSOP, S.A., TYE, K.M. et JACOB, P. (2019). Clustering Time Series wi "
"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. We propose a general statistical framework for clustering multiple time series that exhibit nonlinea "
"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. We propose a general statistical framework for clustering multiple time series that exhibit nonlinea "
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-03-16T10:21:40.000Z"
"docTitle" => "Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approac "
"docSurtitle" => "Conference Proceedings"
"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. <a href="/cv/heng-jeremy">HENG Jeremy</a>, <a href="/cv/jacob-pierre">JACOB Pierre</a>, LIN A, ZHANG "
"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">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2019</span> <span class="document-property-authors">HENG Jeremy, JACOB Pierre, LIN A, ZHANG Y., ALLSOP S. A., TY "
"keywordList" => ""
"docPreview" => "<b>Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach</b><br><span>2019-04 | Conference Proceedings </span> <b>Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Appr "
"docType" => "research"
"publicationLink" => "<a href="#" target="_blank">Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach</a> <a href="#" target="_blank">Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametri "
]
+lang: "en"
+"_type": "_doc"
+"_score": 9.080839
+"parent": null
}
