Essec\Faculty\Model\Profile {#2233
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    "email" => "pierre.jacob@essec.edu"
    "status" => "ACTIF"
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      1 => Essec\Faculty\Model\CareerItem {#2248
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      2 => Essec\Faculty\Model\CareerItem {#2249
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      3 => Essec\Faculty\Model\CareerItem {#2250
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      }
      4 => Essec\Faculty\Model\CareerItem {#2251
        #_index: null
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      0 => Essec\Faculty\Model\Diplome {#2235
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      1 => Essec\Faculty\Model\Diplome {#2237
        #_index: null
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    "bio" => array:2 [
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      "en" => "<p>Pierre E Jacob is a Full Professor at ESSEC Business School, in the Information Systems, Decision Sciences and Statistics (IDS) Department. His interests include time series and prediction, Monte Carlo methods, Markov chains,&nbsp;Bayesian statistics, and probabilistic modelling.&nbsp;</p>"
    ]
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    ]
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      "en" => "Probability Theory & Mathematical Statistics - Statistical Data Analysis"
    ]
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    ]
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      0 => Essec\Faculty\Model\Distinction {#2252
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          ]
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      }
      1 => Essec\Faculty\Model\Distinction {#2253
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      3 => Essec\Faculty\Model\Distinction {#2255
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      4 => Essec\Faculty\Model\Distinction {#2256
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      5 => Essec\Faculty\Model\Distinction {#2257
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      6 => Essec\Faculty\Model\Distinction {#2258
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      8 => Essec\Faculty\Model\Distinction {#2260
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      9 => Essec\Faculty\Model\Distinction {#2261
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      1 => Essec\Faculty\Model\TeachingItem {#2241
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      2 => Essec\Faculty\Model\TeachingItem {#2240
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      3 => Essec\Faculty\Model\TeachingItem {#2238
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      4 => Essec\Faculty\Model\TeachingItem {#2245
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      5 => Essec\Faculty\Model\TeachingItem {#2246
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      6 => Essec\Faculty\Model\TeachingItem {#2243
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      7 => Essec\Faculty\Model\TeachingItem {#2244
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      8 => Essec\Faculty\Model\TeachingItem {#2232
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        #_source: array:7 [
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      9 => Essec\Faculty\Model\TeachingItem {#2236
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            "en" => "Information Systems, Decision Sciences and Statistics"
          ]
          "institution" => array:2 [
            "fr" => "Harvard University"
            "en" => "Harvard University"
          ]
          "country" => array:2 [
            "fr" => "États-Unis"
            "en" => "United States of America"
          ]
        ]
        +lang: "fr"
      }
      10 => Essec\Faculty\Model\TeachingItem {#2239
        #_index: null
        #_id: null
        #_source: array:7 [
          "startDate" => "2013"
          "endDate" => "2015"
          "program" => null
          "label" => array:2 [
            "fr" => "Advanced Simulation"
            "en" => "Advanced Simulation"
          ]
          "type" => array:2 [
            "fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
            "en" => "Information Systems, Decision Sciences and Statistics"
          ]
          "institution" => array:2 [
            "fr" => "University of Oxford"
            "en" => "University of Oxford"
          ]
          "country" => array:2 [
            "fr" => "Royaume-Uni"
            "en" => "United Kingdom"
          ]
        ]
        +lang: "fr"
      }
    ]
    "otherActivities" => array:1 [
      0 => Essec\Faculty\Model\ExtraActivity {#2234
        #_index: null
        #_id: null
        #_source: array:9 [
          "startDate" => "2022-01-01"
          "endDate" => null
          "year" => null
          "uuid" => "501"
          "type" => array:2 [
            "fr" => "Activités professionnelles"
            "en" => "Professional activities"
          ]
          "subType" => array:2 [
            "fr" => "Membre d'une association professionnelle, d'un groupe d'experts ou d'un conseil d'administration"
            "en" => "Member of a professional association, of an expert group or of a board of directors"
          ]
          "label" => array:2 [
            "fr" => "Research Section Committee of the Royal Statistical Society"
            "en" => "Research Section Committee of the Royal Statistical Society"
          ]
          "institution" => array:2 [
            "fr" => null
            "en" => null
          ]
          "country" => array:2 [
            "fr" => "Royaume-Uni"
            "en" => "United Kingdom"
          ]
        ]
        +lang: "fr"
        +"parent": Essec\Faculty\Model\Profile {#2233}
      }
    ]
    "theses" => array:4 [
      0 => Essec\Faculty\Model\These {#2262
        #_index: null
        #_id: null
        #_source: array:9 [
          "year" => "2019"
          "startDate" => null
          "endDate" => "2019"
          "student" => "BERNTON E."
          "firstJob" => ""
          "label" => array:2 [
            "fr" => "Optimal Transport in Statistical Inference and Computation"
            "en" => "Optimal Transport in Statistical Inference and Computation"
          ]
          "role" => array:2 [
            "fr" => "Directeur de thèse"
            "en" => "Thesis director"
          ]
          "institution" => array:2 [
            "fr" => "Harvard University"
            "en" => "Harvard University"
          ]
          "country" => array:2 [
            "fr" => "États-Unis"
            "en" => "United States of America"
          ]
        ]
        +lang: "fr"
        +"parent": Essec\Faculty\Model\Profile {#2233}
      }
      1 => Essec\Faculty\Model\These {#2263
        #_index: null
        #_id: null
        #_source: array:9 [
          "year" => "2021"
          "startDate" => null
          "endDate" => "2021"
          "student" => "JU N."
          "firstJob" => ""
          "label" => array:2 [
            "fr" => "Statistical Methods in Infectious Disease Modeling: Agent-based Models, Computations, and Selection Biases"
            "en" => "Statistical Methods in Infectious Disease Modeling: Agent-based Models, Computations, and Selection Biases"
          ]
          "role" => array:2 [
            "fr" => "Directeur de thèse"
            "en" => "Thesis director"
          ]
          "institution" => array:2 [
            "fr" => "Harvard University"
            "en" => "Harvard University"
          ]
          "country" => array:2 [
            "fr" => "États-Unis"
            "en" => "United States of America"
          ]
        ]
        +lang: "fr"
        +"parent": Essec\Faculty\Model\Profile {#2233}
      }
      2 => Essec\Faculty\Model\These {#2264
        #_index: null
        #_id: null
        #_source: array:9 [
          "year" => "2021"
          "startDate" => null
          "endDate" => "2021"
          "student" => "O'LEARY J."
          "firstJob" => ""
          "label" => array:2 [
            "fr" => "Coupling and Parallelization in Statistical Inference"
            "en" => "Coupling and Parallelization in Statistical Inference"
          ]
          "role" => array:2 [
            "fr" => "Directeur de thèse"
            "en" => "Thesis director"
          ]
          "institution" => array:2 [
            "fr" => "Harvard University"
            "en" => "Harvard University"
          ]
          "country" => array:2 [
            "fr" => "États-Unis"
            "en" => "United States of America"
          ]
        ]
        +lang: "fr"
        +"parent": Essec\Faculty\Model\Profile {#2233}
      }
      3 => Essec\Faculty\Model\These {#2265
        #_index: null
        #_id: null
        #_source: array:9 [
          "year" => "2019"
          "startDate" => null
          "endDate" => "2019"
          "student" => "SHAO S."
          "firstJob" => ""
          "label" => array:2 [
            "fr" => "Bayesian model comparison and asymptotics for state-space models"
            "en" => "Bayesian model comparison and asymptotics for state-space models"
          ]
          "role" => array:2 [
            "fr" => "Directeur de thèse"
            "en" => "Thesis director"
          ]
          "institution" => array:2 [
            "fr" => "Harvard University"
            "en" => "Harvard University"
          ]
          "country" => array:2 [
            "fr" => "États-Unis"
            "en" => "United States of America"
          ]
        ]
        +lang: "fr"
        +"parent": Essec\Faculty\Model\Profile {#2233}
      }
    ]
    "indexedAt" => "2024-12-22T02:21:22.000Z"
    "contributions" => array:36 [
      0 => Essec\Faculty\Model\Contribution {#2267
        #_index: "academ_contributions"
        #_id: "2797"
        #_source: array:18 [
          "id" => "2797"
          "slug" => "unbiased-hamiltonian-monte-carlo-with-couplings"
          "yearMonth" => "2019-02"
          "year" => "2019"
          "title" => "Unbiased Hamiltonian Monte Carlo with couplings"
          "description" => "HENG, J. et JACOB, P. (2019). Unbiased Hamiltonian Monte Carlo with couplings. <i>Biometrika</i>, 106(2), pp. 287-302."
          "authors" => array:2 [
            0 => array:3 [
              "name" => "HENG Jeremy"
              "bid" => "B00760223"
              "slug" => "heng-jeremy"
            ]
            1 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2023-07-10 17:25:47"
          "publicationUrl" => "https://doi.org/10.1093/biomet/asy074"
          "publicationInfo" => array:3 [
            "pages" => "287-302"
            "volume" => "106"
            "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" => "We propose a method for parallelization of Hamiltonian Monte Carlo estimators. Our approach involves constructing a pair of Hamiltonian Monte Carlo chains that are coupled in such a way that they meet exactly after some random number of iterations. These chains can then be combined so that the resulting estimators are unbiased. This allows us to produce independent replicates in parallel and average them to obtain estimators that are consistent in the limit of the number of replicates, rather than in the usual limit of the number of Markov chain iterations. We investigate the scalability of our coupling in high dimensions on a toy example. The choice of algorithmic parameters and the efficiency of our proposed approach are then illustrated on a logistic regression with 300 covariates and a log-Gaussian Cox point processes model with low- to fine-grained discretizations."
            "en" => "We propose a method for parallelization of Hamiltonian Monte Carlo estimators. Our approach involves constructing a pair of Hamiltonian Monte Carlo chains that are coupled in such a way that they meet exactly after some random number of iterations. These chains can then be combined so that the resulting estimators are unbiased. This allows us to produce independent replicates in parallel and average them to obtain estimators that are consistent in the limit of the number of replicates, rather than in the usual limit of the number of Markov chain iterations. We investigate the scalability of our coupling in high dimensions on a toy example. The choice of algorithmic parameters and the efficiency of our proposed approach are then illustrated on a logistic regression with 300 covariates and a log-Gaussian Cox point processes model with low- to fine-grained discretizations."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      1 => Essec\Faculty\Model\Contribution {#2269
        #_index: "academ_contributions"
        #_id: "4453"
        #_source: array:18 [
          "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, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      2 => Essec\Faculty\Model\Contribution {#2271
        #_index: "academ_contributions"
        #_id: "13992"
        #_source: array:18 [
          "id" => "13992"
          "slug" => "poisson-equation-and-coupled-markov-chains"
          "yearMonth" => "2023-03"
          "year" => "2023"
          "title" => "Poisson Equation and Coupled Markov Chains"
          "description" => "JACOB, P., DOUC, R., LEE, A. et VATS, D. (2023). Poisson Equation and Coupled Markov Chains. Dans: 2023 Bayes4Health & CoSInES Workshop. Oxford."
          "authors" => array:4 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "DOUC Randal"
            ]
            2 => array:1 [
              "name" => "LEE Anthony"
            ]
            3 => array:1 [
              "name" => "VATS Dootika"
            ]
          ]
          "ouvrage" => "2023 Bayes4Health & CoSInES Workshop"
          "keywords" => []
          "updatedAt" => "2023-05-24 01:00:41"
          "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" => ""
            "en" => ""
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      3 => Essec\Faculty\Model\Contribution {#2268
        #_index: "academ_contributions"
        #_id: "13994"
        #_source: array:18 [
          "id" => "13994"
          "slug" => "bayesian-inference-in-models-made-of-modules"
          "yearMonth" => "2022-06"
          "year" => "2022"
          "title" => "Bayesian Inference in Models Made of Modules"
          "description" => "JACOB, P. (2022). Bayesian Inference in Models Made of Modules. Dans: 2022 International Society for Bayesian Analysis (ISBA) World Meeting. Montreal."
          "authors" => array:1 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
          ]
          "ouvrage" => "2022 International Society for Bayesian Analysis (ISBA) World Meeting"
          "keywords" => []
          "updatedAt" => "2023-05-24 01:00:41"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => ""
            "volume" => ""
            "number" => ""
          ]
          "type" => array:2 [
            "fr" => "Invité dans une conférence académique (Keynote speaker)"
            "en" => "Invited speaker at an academic conference"
          ]
          "support_type" => array:2 [
            "fr" => null
            "en" => null
          ]
          "countries" => array:2 [
            "fr" => null
            "en" => null
          ]
          "abstract" => array:2 [
            "fr" => ""
            "en" => ""
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      4 => Essec\Faculty\Model\Contribution {#2272
        #_index: "academ_contributions"
        #_id: "12516"
        #_source: array:18 [
          "id" => "12516"
          "slug" => "unbiased-markov-chain-monte-carlo-methods-with-couplings"
          "yearMonth" => "2020-07"
          "year" => "2020"
          "title" => "Unbiased Markov chain Monte Carlo methods with couplings"
          "description" => "JACOB, P., O’LEARY, J. et ATCHADÉ, Y.F. (2020). Unbiased Markov chain Monte Carlo methods with couplings. <i>Journal of the Royal Statistical Society: Series B (Statistical Methodology)</i>, 82(3), pp. 543-600."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "O’LEARY John"
            ]
            2 => array:1 [
              "name" => "ATCHADÉ Yves F."
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:5 [
            0 => "Coupling estimation"
            1 => "Markov chain"
            2 => "Monte Carlo methods"
            3 => "Parallel computing"
            4 => "Unbiased"
          ]
          "updatedAt" => "2021-09-24 10:33:27"
          "publicationUrl" => "https://rss.onlinelibrary.wiley.com/doi/10.1111/rssb.12336#"
          "publicationInfo" => array:3 [
            "pages" => "543-600"
            "volume" => "82"
            "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" => "Markov chain Monte Carlo (MCMC) methods provide consistent approximations of integrals as the number of iterations goes to ∞. MCMC estimators are generally biased after any fixed number of iterations. We propose to remove this bias by using couplings of Markov chains together with a telescopic sum argument of Glynn and Rhee. The resulting unbiased estimators can be computed independently in parallel. We discuss practical couplings for popular MCMC algorithms. We establish the theoretical validity of the estimators proposed and study their efficiency relative to the underlying MCMC algorithms. Finally, we illustrate the performance and limitations of the method on toy examples, on an Ising model around its critical temperature, on a high dimensional variable-selection problem, and on an approximation of the cut distribution arising in Bayesian inference for models made of multiple modules."
            "en" => "Markov chain Monte Carlo (MCMC) methods provide consistent approximations of integrals as the number of iterations goes to ∞. MCMC estimators are generally biased after any fixed number of iterations. We propose to remove this bias by using couplings of Markov chains together with a telescopic sum argument of Glynn and Rhee. The resulting unbiased estimators can be computed independently in parallel. We discuss practical couplings for popular MCMC algorithms. We establish the theoretical validity of the estimators proposed and study their efficiency relative to the underlying MCMC algorithms. Finally, we illustrate the performance and limitations of the method on toy examples, on an Ising model around its critical temperature, on a high dimensional variable-selection problem, and on an approximation of the cut distribution arising in Bayesian inference for models made of multiple modules."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      5 => Essec\Faculty\Model\Contribution {#2266
        #_index: "academ_contributions"
        #_id: "12518"
        #_source: array:18 [
          "id" => "12518"
          "slug" => "bayesian-model-comparison-with-the-hyvarinen-score-computation-and-consistency"
          "yearMonth" => "2019-09"
          "year" => "2019"
          "title" => "Bayesian Model Comparison with the Hyvärinen Score: Computation and Consistency"
          "description" => "SHAO, S., JACOB, P., DING, J. et TAROKH, V. (2019). Bayesian Model Comparison with the Hyvärinen Score: Computation and Consistency. <i>Journal of the American Statistical Association</i>, 114(528), pp. 1826-1837."
          "authors" => array:4 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "SHAO Stephane"
            ]
            2 => array:1 [
              "name" => "DING Jie"
            ]
            3 => array:1 [
              "name" => "TAROKH Vahid"
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2021-09-24 10:33:27"
          "publicationUrl" => "https://doi.org/10.1080/01621459.2018.1518237"
          "publicationInfo" => array:3 [
            "pages" => "1826-1837"
            "volume" => "114"
            "number" => "528"
          ]
          "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" => "The Bayes factor is a widely used criterion in model comparison and its logarithm is a difference of out-of-sample predictive scores under the logarithmic scoring rule. However, when some of the candidate models involve vague priors on their parameters, the log-Bayes factor features an arbitrary additive constant that hinders its interpretation. As an alternative, we consider model comparison using the Hyvärinen score. We propose a method to consistently estimate this score for parametric models, using sequential Monte Carlo methods. We show that this score can be estimated for models with tractable likelihoods as well as nonlinear non-Gaussian state-space models with intractable likelihoods. We prove the asymptotic consistency of this new model selection criterion under strong regularity assumptions in the case of nonnested models, and we provide qualitative insights for the nested case"
            "en" => "The Bayes factor is a widely used criterion in model comparison and its logarithm is a difference of out-of-sample predictive scores under the logarithmic scoring rule. However, when some of the candidate models involve vague priors on their parameters, the log-Bayes factor features an arbitrary additive constant that hinders its interpretation. As an alternative, we consider model comparison using the Hyvärinen score. We propose a method to consistently estimate this score for parametric models, using sequential Monte Carlo methods. We show that this score can be estimated for models with tractable likelihoods as well as nonlinear non-Gaussian state-space models with intractable likelihoods. We prove the asymptotic consistency of this new model selection criterion under strong regularity assumptions in the case of nonnested models, and we provide qualitative insights for the nested case"
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      6 => Essec\Faculty\Model\Contribution {#2270
        #_index: "academ_contributions"
        #_id: "12517"
        #_source: array:18 [
          "id" => "12517"
          "slug" => "approximate-bayesian-computation-with-the-wasserstein-distance"
          "yearMonth" => "2019-04"
          "year" => "2019"
          "title" => "Approximate Bayesian computation with the Wasserstein distance"
          "description" => "BERNTON, E., JACOB, P., GERBER, M. et ROBERT, C.P. (2019). Approximate Bayesian computation with the Wasserstein distance. <i>Journal of the Royal Statistical Society: Series B (Statistical Methodology)</i>, 81(2), pp. 235-269."
          "authors" => array:4 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "BERNTON Espen"
            ]
            2 => array:1 [
              "name" => "GERBER Mathieu"
            ]
            3 => array:1 [
              "name" => "ROBERT Christian P."
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2021-09-24 10:33:27"
          "publicationUrl" => "https://doi.org/10.1111/rssb.12312"
          "publicationInfo" => array:3 [
            "pages" => "235-269"
            "volume" => "81"
            "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" => "A growing number of generative statistical models do not permit the numerical evaluation of their likelihood functions. Approximate Bayesian computation has become a popular approach to overcome this issue, in which one simulates synthetic data sets given parameters and compares summaries of these data sets with the corresponding observed values. We propose to avoid the use of summaries and the ensuing loss of information by instead using the Wasserstein distance between the empirical distributions of the observed and synthetic data. This generalizes the well-known approach of using order statistics within approximate Bayesian computation to arbitrary dimensions. We describe how recently developed approximations of the Wasserstein distance allow the method to scale to realistic data sizes, and we propose a new distance based on the Hilbert space filling curve. We provide a theoretical study of the method proposed, describing consistency as the threshold goes to 0 while the observations are kept fixed, and concentration properties as the number of observations grows. Various extensions to time series data are discussed. The approach is illustrated on various examples, including univariate and multivariate g-and-k distributions, a toggle switch model from systems biology, a queuing model and a Lévy-driven stochastic volatility model."
            "en" => "A growing number of generative statistical models do not permit the numerical evaluation of their likelihood functions. Approximate Bayesian computation has become a popular approach to overcome this issue, in which one simulates synthetic data sets given parameters and compares summaries of these data sets with the corresponding observed values. We propose to avoid the use of summaries and the ensuing loss of information by instead using the Wasserstein distance between the empirical distributions of the observed and synthetic data. This generalizes the well-known approach of using order statistics within approximate Bayesian computation to arbitrary dimensions. We describe how recently developed approximations of the Wasserstein distance allow the method to scale to realistic data sizes, and we propose a new distance based on the Hilbert space filling curve. We provide a theoretical study of the method proposed, describing consistency as the threshold goes to 0 while the observations are kept fixed, and concentration properties as the number of observations grows. Various extensions to time series data are discussed. The approach is illustrated on various examples, including univariate and multivariate g-and-k distributions, a toggle switch model from systems biology, a queuing model and a Lévy-driven stochastic volatility model."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      7 => Essec\Faculty\Model\Contribution {#2273
        #_index: "academ_contributions"
        #_id: "12519"
        #_source: array:18 [
          "id" => "12519"
          "slug" => "smoothing-with-couplings-of-conditional-particle-filters"
          "yearMonth" => "2020-04"
          "year" => "2020"
          "title" => "Smoothing With Couplings of Conditional Particle Filters"
          "description" => "JACOB, P., LINDSTEN, F. et SCHÖN, T.B. (2020). Smoothing With Couplings of Conditional Particle Filters. <i>Journal of the American Statistical Association</i>, 115(530), pp. 721-729."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "LINDSTEN Fredrik"
            ]
            2 => array:1 [
              "name" => "SCHÖN Thomas B."
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:5 [
            0 => "Couplings"
            1 => "Debiasing techniques"
            2 => "Parallel computation"
            3 => "Particle filtering"
            4 => "Particle smoothing"
          ]
          "updatedAt" => "2021-09-24 10:33:27"
          "publicationUrl" => "https://www.tandfonline.com/doi/full/10.1080/01621459.2018.1548856"
          "publicationInfo" => array:3 [
            "pages" => "721-729"
            "volume" => "115"
            "number" => "530"
          ]
          "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" => "In state–space models, smoothing refers to the task of estimating a latent stochastic process given noisy measurements related to the process. We propose an unbiased estimator of smoothing expectations. The lack-of-bias property has methodological benefits: independent estimators can be generated in parallel, and CI can be constructed from the central limit theorem to quantify the approximation error. To design unbiased estimators, we combine a generic debiasing technique for Markov chains, with a Markov chain Monte Carlo algorithm for smoothing. The resulting procedure is widely applicable and we show in numerical experiments that the removal of the bias comes at a manageable increase in variance. We establish the validity of the proposed estimators under mild assumptions. Numerical experiments are provided on toy models, including a setting of highly informative observations, and for a realistic Lotka–Volterra model with an intractable transition density. Supplementary materials for this article are available online."
            "en" => "In state–space models, smoothing refers to the task of estimating a latent stochastic process given noisy measurements related to the process. We propose an unbiased estimator of smoothing expectations. The lack-of-bias property has methodological benefits: independent estimators can be generated in parallel, and CI can be constructed from the central limit theorem to quantify the approximation error. To design unbiased estimators, we combine a generic debiasing technique for Markov chains, with a Markov chain Monte Carlo algorithm for smoothing. The resulting procedure is widely applicable and we show in numerical experiments that the removal of the bias comes at a manageable increase in variance. We establish the validity of the proposed estimators under mild assumptions. Numerical experiments are provided on toy models, including a setting of highly informative observations, and for a realistic Lotka–Volterra model with an intractable transition density. Supplementary materials for this article are available online."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      8 => Essec\Faculty\Model\Contribution {#2274
        #_index: "academ_contributions"
        #_id: "12520"
        #_source: array:18 [
          "id" => "12520"
          "slug" => "estimating-relatedness-between-malaria-parasites"
          "yearMonth" => "2019-08"
          "year" => "2019"
          "title" => "Estimating Relatedness Between Malaria Parasites"
          "description" => "TAYLOR, A.R., JACOB, P., NEAFSEY, D.E. et BUCKEE, C.O. (2019). Estimating Relatedness Between Malaria Parasites. <i>Genetics</i>, 212(4), pp. 1337-1351."
          "authors" => array:4 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "TAYLOR Aimee R"
            ]
            2 => array:1 [
              "name" => "NEAFSEY Daniel E"
            ]
            3 => array:1 [
              "name" => "BUCKEE Caroline O"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:9 [
            0 => "Plasmodium falciparum "
            1 => "Plasmodium vivax "
            2 => "genetic epidemiology "
            3 => "hidden Markov model "
            4 => "identity-by-descent "
            5 => "identity-by-state "
            6 => "independence model "
            7 => "malaria "
            8 => "relatedness."
          ]
          "updatedAt" => "2021-09-24 10:33:27"
          "publicationUrl" => "https://doi.org/10.1534/genetics.119.302120"
          "publicationInfo" => array:3 [
            "pages" => "1337-1351"
            "volume" => "212"
            "number" => "4"
          ]
          "type" => array:2 [
            "fr" => "Articles"
            "en" => "Journal articles"
          ]
          "support_type" => array:2 [
            "fr" => "Revue scientifique"
            "en" => "Scientific journal"
          ]
          "countries" => array:2 [
            "fr" => "États-Unis"
            "en" => "United States of America"
          ]
          "abstract" => array:2 [
            "fr" => "Understanding the relatedness of individuals within or between populations is a common goal in biology. Increasingly, relatedness features in genetic epidemiology studies of pathogens. These studies are relatively new compared to those in humans and other organisms, but are important for designing interventions and understanding pathogen transmission. Only recently have researchers begun to routinely apply relatedness to apicomplexan eukaryotic malaria parasites, and to date have used a range of different approaches on an ad hoc basis. Therefore, it remains unclear how to compare different studies and which measures to use. Here, we systematically compare measures based on identity-by-state (IBS) and identity-by-descent (IBD) using a globally diverse data set of malaria parasites, Plasmodium falciparum and P. vivax, and provide marker requirements for estimates based on IBD. We formally show that the informativeness of polyallelic markers for relatedness inference is maximized when alleles are equifrequent."
            "en" => "Understanding the relatedness of individuals within or between populations is a common goal in biology. Increasingly, relatedness features in genetic epidemiology studies of pathogens. These studies are relatively new compared to those in humans and other organisms, but are important for designing interventions and understanding pathogen transmission. Only recently have researchers begun to routinely apply relatedness to apicomplexan eukaryotic malaria parasites, and to date have used a range of different approaches on an ad hoc basis. Therefore, it remains unclear how to compare different studies and which measures to use. Here, we systematically compare measures based on identity-by-state (IBS) and identity-by-descent (IBD) using a globally diverse data set of malaria parasites, Plasmodium falciparum and P. vivax, and provide marker requirements for estimates based on IBD. We formally show that the informativeness of polyallelic markers for relatedness inference is maximized when alleles are equifrequent."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      9 => Essec\Faculty\Model\Contribution {#2275
        #_index: "academ_contributions"
        #_id: "12521"
        #_source: array:18 [
          "id" => "12521"
          "slug" => "a-gibbs-sampler-for-a-class-of-random-convex-polytopes"
          "yearMonth" => "2021-04"
          "year" => "2021"
          "title" => "A Gibbs Sampler for a Class of Random Convex Polytopes"
          "description" => "JACOB, P., GONG, R., EDLEFSEN, P.T. et DEMPSTER, A.P. (2021). A Gibbs Sampler for a Class of Random Convex Polytopes. <i>Journal of the American Statistical Association</i>, 116(535), pp. 1181-1192."
          "authors" => array:4 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "GONG Ruobin"
            ]
            2 => array:1 [
              "name" => "EDLEFSEN Paul T."
            ]
            3 => array:1 [
              "name" => "DEMPSTER Arthur P."
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:3 [
            0 => "Algorithms"
            1 => "Bayesian methods"
            2 => "Categorical data analysis"
          ]
          "updatedAt" => "2022-06-03 10:42:42"
          "publicationUrl" => "https://www.tandfonline.com/doi/abs/10.1080/01621459.2021.1881523?journalCode=uasa20"
          "publicationInfo" => array:3 [
            "pages" => "1181-1192"
            "volume" => "116"
            "number" => "535"
          ]
          "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" => "We present a Gibbs sampler for the Dempster–Shafer (DS) approach to statistical inference for categorical distributions. The DS framework extends the Bayesian approach, allows in particular the use of partial prior information, and yields three-valued uncertainty assessments representing probabilities “for,” “against,” and “don’t know” about formal assertions of interest. The proposed algorithm targets the distribution of a class of random convex polytopes which encapsulate the DS inference. The sampler relies on an equivalence between the iterative constraints of the vertex configuration and the nonnegativity of cycles in a fully connected directed graph. Illustrations include the testing of independence in 2 × 2 contingency tables and parameter estimation of the linkage model."
            "en" => "We present a Gibbs sampler for the Dempster–Shafer (DS) approach to statistical inference for categorical distributions. The DS framework extends the Bayesian approach, allows in particular the use of partial prior information, and yields three-valued uncertainty assessments representing probabilities “for,” “against,” and “don’t know” about formal assertions of interest. The proposed algorithm targets the distribution of a class of random convex polytopes which encapsulate the DS inference. The sampler relies on an equivalence between the iterative constraints of the vertex configuration and the nonnegativity of cycles in a fully connected directed graph. Illustrations include the testing of independence in 2 × 2 contingency tables and parameter estimation of the linkage model."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      10 => Essec\Faculty\Model\Contribution {#2276
        #_index: "academ_contributions"
        #_id: "12533"
        #_source: array:18 [
          "id" => "12533"
          "slug" => "maximal-couplings-of-the-metropolis-hastings-algorithm"
          "yearMonth" => "2021-01"
          "year" => "2021"
          "title" => "Maximal Couplings of the Metropolis-Hastings Algorithm"
          "description" => "JACOB, P., WANG, G. et O'LEARY, J. (2021). Maximal Couplings of the Metropolis-Hastings Algorithm. Dans: <i>The 24th International Conference on Artificial Intelligence and Statistics</i>. Proceedings of Machine Learning Research, pp. 1225-1233."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "WANG Guanyang"
            ]
            2 => array:1 [
              "name" => "O'LEARY John"
            ]
          ]
          "ouvrage" => "The 24th International Conference on Artificial Intelligence and Statistics"
          "keywords" => []
          "updatedAt" => "2021-09-24 10:33:27"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => "1225-1233"
            "volume" => "130"
            "number" => null
          ]
          "type" => array:2 [
            "fr" => "Actes d'une conférence"
            "en" => "Conference Proceedings"
          ]
          "support_type" => array:2 [
            "fr" => "Editeur"
            "en" => "Publisher"
          ]
          "countries" => array:2 [
            "fr" => "Royaume-Uni"
            "en" => "United Kingdom"
          ]
          "abstract" => array:2 [
            "fr" => null
            "en" => null
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      11 => Essec\Faculty\Model\Contribution {#2277
        #_index: "academ_contributions"
        #_id: "12534"
        #_source: array:18 [
          "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" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      12 => Essec\Faculty\Model\Contribution {#2278
        #_index: "academ_contributions"
        #_id: "12535"
        #_source: array:18 [
          "id" => "12535"
          "slug" => "unbiased-markov-chain-monte-carlo-for-intractable-target-distributions"
          "yearMonth" => "2020-01"
          "year" => "2020"
          "title" => "Unbiased Markov chain Monte Carlo for intractable target distributions"
          "description" => "MIDDLETON, L., DELIGIANNIDIS, G., DOUCET, A. et JACOB, P. (2020). Unbiased Markov chain Monte Carlo for intractable target distributions. <i>The Electronic Journal of Statistics</i>, 14(2), pp. 2842-2891."
          "authors" => array:4 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "MIDDLETON Lawrence"
            ]
            2 => array:1 [
              "name" => "DELIGIANNIDIS George"
            ]
            3 => array:1 [
              "name" => "DOUCET Arnaud"
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2021-09-24 10:33:27"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => "2842-2891"
            "volume" => "14"
            "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" => null
            "en" => null
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      13 => Essec\Faculty\Model\Contribution {#2279
        #_index: "academ_contributions"
        #_id: "12536"
        #_source: array:18 [
          "id" => "12536"
          "slug" => "on-parameter-estimation-with-the-wasserstein-distance"
          "yearMonth" => "2019-10"
          "year" => "2019"
          "title" => "On parameter estimation with the Wasserstein distance"
          "description" => "BERNTON, E., JACOB, P., GERBER, M. et ROBERT, C.P. (2019). On parameter estimation with the Wasserstein distance. <i>Information and Inference: A Journal of the IMA</i>, 8(4), pp. 657-676."
          "authors" => array:4 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "BERNTON Espen"
            ]
            2 => array:1 [
              "name" => "GERBER Mathieu"
            ]
            3 => array:1 [
              "name" => "ROBERT Christian P"
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2021-09-24 10:33:27"
          "publicationUrl" => "https://doi.org/10.1093/imaiai/iaz003"
          "publicationInfo" => array:3 [
            "pages" => "657-676"
            "volume" => "8"
            "number" => "4"
          ]
          "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" => "Statistical inference can be performed by minimizing, over the parameter space, the Wasserstein distance between model distributions and the empirical distribution of the data. We study asymptotic properties of such minimum Wasserstein distance estimators, complementing results derived by Bassetti, Bodini and Regazzini in 2006. In particular, our results cover the misspecified setting, in which the data-generating process is not assumed to be part of the family of distributions described by the model. Our results are motivated by recent applications of minimum Wasserstein estimators to complex generative models. We discuss some difficulties arising in the numerical approximation of these estimators. Two of our numerical examples (⁠g-and-κ and sum of log-normals) are taken from the literature on approximate Bayesian computation and have likelihood functions that are not analytically tractable. Two other examples involve misspecified models."
            "en" => "Statistical inference can be performed by minimizing, over the parameter space, the Wasserstein distance between model distributions and the empirical distribution of the data. We study asymptotic properties of such minimum Wasserstein distance estimators, complementing results derived by Bassetti, Bodini and Regazzini in 2006. In particular, our results cover the misspecified setting, in which the data-generating process is not assumed to be part of the family of distributions described by the model. Our results are motivated by recent applications of minimum Wasserstein estimators to complex generative models. We discuss some difficulties arising in the numerical approximation of these estimators. Two of our numerical examples (⁠g-and-κ and sum of log-normals) are taken from the literature on approximate Bayesian computation and have likelihood functions that are not analytically tractable. Two other examples involve misspecified models."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      14 => Essec\Faculty\Model\Contribution {#2280
        #_index: "academ_contributions"
        #_id: "12537"
        #_source: array:18 [
          "id" => "12537"
          "slug" => "clustering-time-series-with-nonlinear-dynamics-a-bayesian-non-parametric-and-particle-based-approach"
          "yearMonth" => "2019-01"
          "year" => "2019"
          "title" => "Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach"
          "description" => "JACOB, P., LIN, A., ZHANG, Y., HENG, J., ALLSOP, S.A., TYE, K.M. et BA, D. (2019). Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach. Dans: <i>The 22nd International Conference on Artificial Intelligence and Statistics</i>. Proceedings of Machine Learning Research, pp. 2476-2484."
          "authors" => array:7 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:3 [
              "name" => "HENG Jeremy"
              "bid" => "B00760223"
              "slug" => "heng-jeremy"
            ]
            2 => array:1 [
              "name" => "LIN Alexander"
            ]
            3 => array:1 [
              "name" => "ZHANG Yingzhuo"
            ]
            4 => array:1 [
              "name" => "ALLSOP Stephen A."
            ]
            5 => array:1 [
              "name" => "TYE K. M."
            ]
            6 => array:1 [
              "name" => "BA Demba"
            ]
          ]
          "ouvrage" => "The 22nd International Conference on Artificial Intelligence and Statistics"
          "keywords" => []
          "updatedAt" => "2021-09-24 10:33:27"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => "2476-2484"
            "volume" => "89"
            "number" => null
          ]
          "type" => array:2 [
            "fr" => "Actes d'une conférence"
            "en" => "Conference Proceedings"
          ]
          "support_type" => array:2 [
            "fr" => "Editeur"
            "en" => "Publisher"
          ]
          "countries" => array:2 [
            "fr" => "Royaume-Uni"
            "en" => "United Kingdom"
          ]
          "abstract" => array:2 [
            "fr" => null
            "en" => null
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      15 => Essec\Faculty\Model\Contribution {#2281
        #_index: "academ_contributions"
        #_id: "12538"
        #_source: array:18 [
          "id" => "12538"
          "slug" => "estimating-convergence-of-markov-chains-with-l-lag-couplings"
          "yearMonth" => "2019-01"
          "year" => "2019"
          "title" => "Estimating Convergence of Markov chains with L-Lag Couplings"
          "description" => "JACOB, P., BISWAS, N. et VANETTI, P. (2019). Estimating Convergence of Markov chains with L-Lag Couplings. Dans: <i>Advances in Neural Information Processing Systems</i>. Curran Associates, Inc."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "BISWAS Niloy"
            ]
            2 => array:1 [
              "name" => "VANETTI Paul"
            ]
          ]
          "ouvrage" => "Advances in Neural Information Processing Systems"
          "keywords" => []
          "updatedAt" => "2021-09-24 10:33:27"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => null
            "volume" => "32"
            "number" => null
          ]
          "type" => array:2 [
            "fr" => "Actes d'une conférence"
            "en" => "Conference Proceedings"
          ]
          "support_type" => array:2 [
            "fr" => "Editeur"
            "en" => "Publisher"
          ]
          "countries" => array:2 [
            "fr" => "États-Unis"
            "en" => "United States of America"
          ]
          "abstract" => array:2 [
            "fr" => null
            "en" => null
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      16 => Essec\Faculty\Model\Contribution {#2282
        #_index: "academ_contributions"
        #_id: "12539"
        #_source: array:18 [
          "id" => "12539"
          "slug" => "unbiased-smoothing-using-particle-independent-metropolis-hastings"
          "yearMonth" => "2019-01"
          "year" => "2019"
          "title" => "Unbiased Smoothing using Particle Independent Metropolis-Hastings"
          "description" => "JACOB, P., MIDDLETON, L., DELIGIANNIDIS, G. et DOUCET, A. (2019). Unbiased Smoothing using Particle Independent Metropolis-Hastings. Dans: <i>The 22nd International Conference on Artificial Intelligence and Statistics</i>. Proceedings of Machine Learning Research, pp. 2378-2387."
          "authors" => array:4 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "MIDDLETON Lawrence"
            ]
            2 => array:1 [
              "name" => "DELIGIANNIDIS G."
            ]
            3 => array:1 [
              "name" => "DOUCET Arnaud"
            ]
          ]
          "ouvrage" => "The 22nd International Conference on Artificial Intelligence and Statistics"
          "keywords" => []
          "updatedAt" => "2021-09-24 10:33:27"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => "2378-2387"
            "volume" => "89"
            "number" => null
          ]
          "type" => array:2 [
            "fr" => "Actes d'une conférence"
            "en" => "Conference Proceedings"
          ]
          "support_type" => array:2 [
            "fr" => "Editeur"
            "en" => "Publisher"
          ]
          "countries" => array:2 [
            "fr" => "Royaume-Uni"
            "en" => "United Kingdom"
          ]
          "abstract" => array:2 [
            "fr" => null
            "en" => null
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      17 => Essec\Faculty\Model\Contribution {#2283
        #_index: "academ_contributions"
        #_id: "12540"
        #_source: array:18 [
          "id" => "12540"
          "slug" => "bayesian-inference-in-non-markovian-state-space-models-with-applications-to-battery-fractional-order-systems"
          "yearMonth" => "2018-03"
          "year" => "2018"
          "title" => "Bayesian Inference in Non-Markovian State-Space Models With Applications to Battery Fractional-Order Systems"
          "description" => "JACOB, P., ALAVI, S.M.M., MAHDI, A., PAYNE, S.J. et HOWEY, D.A. (2018). Bayesian Inference in Non-Markovian State-Space Models With Applications to Battery Fractional-Order Systems. <i>IEEE Transactions on Control Systems Technology</i>, 26(2), pp. 497-506."
          "authors" => array:5 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "ALAVI Seyed Mohammad Mahdi"
            ]
            2 => array:1 [
              "name" => "MAHDI Adam"
            ]
            3 => array:1 [
              "name" => "PAYNE Stephen J."
            ]
            4 => array:1 [
              "name" => "HOWEY David A."
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2021-09-24 10:33:27"
          "publicationUrl" => "https://ieeexplore.ieee.org/document/7873246"
          "publicationInfo" => array:3 [
            "pages" => "497-506"
            "volume" => "26"
            "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" => "Battery impedance spectroscopy models are given by fractional-order (FO) differential equations. In the discrete-time domain, they give rise to state-space models where the latent process is not Markovian. Parameter estimation for these models is, therefore, challenging, especially for noncommensurate FO models. In this paper, we propose a Bayesian approach to identify the parameters of generic FO systems. The computational challenge is tackled with particle Markov chain Monte Carlo methods, with an implementation specifically designed for the non-Markovian setting."
            "en" => "Battery impedance spectroscopy models are given by fractional-order (FO) differential equations. In the discrete-time domain, they give rise to state-space models where the latent process is not Markovian. Parameter estimation for these models is, therefore, challenging, especially for noncommensurate FO models. In this paper, we propose a Bayesian approach to identify the parameters of generic FO systems. The computational challenge is tackled with particle Markov chain Monte Carlo methods, with an implementation specifically designed for the non-Markovian setting."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      18 => Essec\Faculty\Model\Contribution {#2284
        #_index: "academ_contributions"
        #_id: "12541"
        #_source: array:18 [
          "id" => "12541"
          "slug" => "parallel-resampling-in-the-particle-filter"
          "yearMonth" => "2016-01"
          "year" => "2016"
          "title" => "Parallel Resampling in the Particle Filter"
          "description" => "MURRAY, L.M., LEE, A. et JACOB, P. (2016). Parallel Resampling in the Particle Filter. <i>Journal of Computational and Graphical Statistics</i>, 25(3), pp. 789-805."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "MURRAY Lawrence M."
            ]
            2 => array:1 [
              "name" => "LEE Anthony"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:4 [
            0 => "Graphics processing unit"
            1 => "parallel computing"
            2 => "particle methods"
            3 => "Sequential Monte Carlo"
          ]
          "updatedAt" => "2023-01-27 01:00:40"
          "publicationUrl" => "https://doi.org/10.1080/10618600.2015.1062015"
          "publicationInfo" => array:3 [
            "pages" => "789-805"
            "volume" => "25"
            "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" => "Modern parallel computing devices, such as the graphics processing unit (GPU), have gained significant traction in scientific and statistical computing. They are particularly well-suited to data-parallel algorithms such as the particle filter, or more generally sequential Monte Carlo (SMC), which are increasingly used in statistical inference. SMC methods carry a set of weighted particles through repeated propagation, weighting, and resampling steps."
            "en" => "Modern parallel computing devices, such as the graphics processing unit (GPU), have gained significant traction in scientific and statistical computing. They are particularly well-suited to data-parallel algorithms such as the particle filter, or more generally sequential Monte Carlo (SMC), which are increasingly used in statistical inference. SMC methods carry a set of weighted particles through repeated propagation, weighting, and resampling steps."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      19 => Essec\Faculty\Model\Contribution {#2285
        #_index: "academ_contributions"
        #_id: "12542"
        #_source: array:18 [
          "id" => "12542"
          "slug" => "path-storage-in-the-particle-filter"
          "yearMonth" => "2015-03"
          "year" => "2015"
          "title" => "Path storage in the particle filter"
          "description" => "JACOB, P., MURRAY, L.M. et RUBENTHALER, S. (2015). Path storage in the particle filter. <i>Statistics and Computing</i>, 25(2), pp. 487-496."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "MURRAY Lawrence M."
            ]
            2 => array:1 [
              "name" => "RUBENTHALER Sylvain"
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2023-01-27 01:00:40"
          "publicationUrl" => "https://link.springer.com/article/10.1007/s11222-013-9445-x"
          "publicationInfo" => array:3 [
            "pages" => "487-496"
            "volume" => "25"
            "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" => "This article considers the problem of storing the paths generated by a particle filter and more generally by a sequential Monte Carlo algorithm. It provides a theoretical result bounding the expected memory cost by T+CNlogN where T is the time horizon, N is the number of particles and C is a constant, as well as an efficient algorithm to realise this. The theoretical result and the algorithm are illustrated with numerical experiments."
            "en" => "This article considers the problem of storing the paths generated by a particle filter and more generally by a sequential Monte Carlo algorithm. It provides a theoretical result bounding the expected memory cost by T+CNlogN where T is the time horizon, N is the number of particles and C is a constant, as well as an efficient algorithm to realise this. The theoretical result and the algorithm are illustrated with numerical experiments."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      20 => Essec\Faculty\Model\Contribution {#2286
        #_index: "academ_contributions"
        #_id: "12543"
        #_source: array:18 [
          "id" => "12543"
          "slug" => "on-nonnegative-unbiased-estimators"
          "yearMonth" => "2015-01"
          "year" => "2015"
          "title" => "On nonnegative unbiased estimators"
          "description" => "JACOB, P. et THIERY, A.H. (2015). On nonnegative unbiased estimators. <i>Annals of Statistics</i>, 43(2)."
          "authors" => array:2 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "THIERY Alexandre H."
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2023-01-27 01:00:40"
          "publicationUrl" => "https://doi.org/10.1214/15-AOS1311"
          "publicationInfo" => array:3 [
            "pages" => ""
            "volume" => "43"
            "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" => "The study is motivated by pseudo-marginal Monte Carlo algorithms that rely on such nonnegative unbiased estimators. These methods allow “exact inference” in intractable models, in the sense that integrals with respect to a target distribution can be estimated without any systematic error, even though the associated probability density function cannot be evaluated pointwise. We discuss the consequences of our results on the applicability of pseudo-marginal algorithms and thus on the possibility of exact inference in intractable models."
            "en" => "The study is motivated by pseudo-marginal Monte Carlo algorithms that rely on such nonnegative unbiased estimators. These methods allow “exact inference” in intractable models, in the sense that integrals with respect to a target distribution can be estimated without any systematic error, even though the associated probability density function cannot be evaluated pointwise. We discuss the consequences of our results on the applicability of pseudo-marginal algorithms and thus on the possibility of exact inference in intractable models."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      21 => Essec\Faculty\Model\Contribution {#2287
        #_index: "academ_contributions"
        #_id: "12544"
        #_source: array:18 [
          "id" => "12544"
          "slug" => "the-wang-landau-algorithm-reaches-the-flat-histogram-criterion-in-finite-time"
          "yearMonth" => "2014-01"
          "year" => "2014"
          "title" => "The Wang–Landau algorithm reaches the flat histogram criterion in finite time"
          "description" => "JACOB, P. et RYDER, R.J. (2014). The Wang–Landau algorithm reaches the flat histogram criterion in finite time. <i>Annals of Applied Probability</i>, 24(1), pp. 34-53."
          "authors" => array:2 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "RYDER Robin J."
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2023-01-27 01:00:40"
          "publicationUrl" => "https://doi.org/10.1214/12-AAP913"
          "publicationInfo" => array:3 [
            "pages" => "34-53"
            "volume" => "24"
            "number" => "1"
          ]
          "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" => "The Wang–Landau algorithm aims at sampling from a probability distribution, while penalizing some regions of the state space and favoring others. It is widely used, but its convergence properties are still unknown. We show that for some variations of the algorithm, the Wang–Landau algorithm reaches the so-called flat histogram criterion in finite time, and that this criterion can be never reached for other variations. The arguments are shown in a simple context—compact spaces, density functions bounded from both sides—for the sake of clarity, and could be extended to more general contexts."
            "en" => "The Wang–Landau algorithm aims at sampling from a probability distribution, while penalizing some regions of the state space and favoring others. It is widely used, but its convergence properties are still unknown. We show that for some variations of the algorithm, the Wang–Landau algorithm reaches the so-called flat histogram criterion in finite time, and that this criterion can be never reached for other variations. The arguments are shown in a simple context—compact spaces, density functions bounded from both sides—for the sake of clarity, and could be extended to more general contexts."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      22 => Essec\Faculty\Model\Contribution {#2288
        #_index: "academ_contributions"
        #_id: "12545"
        #_source: array:18 [
          "id" => "12545"
          "slug" => "smc2-an-efficient-algorithm-for-sequential-analysis-of-state-space-models"
          "yearMonth" => "2013-06"
          "year" => "2013"
          "title" => "SMC2: an efficient algorithm for sequential analysis of state space models"
          "description" => "CHOPIN, N., JACOB, P. et PAPASPILIOPOULOS, O. (2013). SMC2: an efficient algorithm for sequential analysis of state space models. <i>Journal of the Royal Statistical Society: Series B (Statistical Methodology)</i>, 75(3), pp. 397-426."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "CHOPIN N."
            ]
            2 => array:1 [
              "name" => "PAPASPILIOPOULOS O."
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2023-01-27 01:00:40"
          "publicationUrl" => "https://doi.org/10.1111/j.1467-9868.2012.01046.x"
          "publicationInfo" => array:3 [
            "pages" => "397-426"
            "volume" => "75"
            "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" => "We consider the generic problem of performing sequential Bayesian inference in a state space model with observation process y, state process x and fixed parameter θ."
            "en" => "We consider the generic problem of performing sequential Bayesian inference in a state space model with observation process y, state process x and fixed parameter θ."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      23 => Essec\Faculty\Model\Contribution {#2289
        #_index: "academ_contributions"
        #_id: "12702"
        #_source: array:18 [
          "id" => "12702"
          "slug" => "evaluating-the-reliability-of-mobility-metrics-from-aggregated-mobile-phone-data-as-proxies-for-sars-cov-2-transmission-in-the-usa-a-population-based-study"
          "yearMonth" => "2022-01"
          "year" => "2022"
          "title" => "Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study"
          "description" => "KISHORE, N., TAYLOR, A.R., JACOB, P., VEMBAR, N., COHEN, T., BUCKEE, C.O. et MENZIES, N.A. (2022). Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study. <i>The Lancet Digital Health</i>, 4(1), pp. e27-e36."
          "authors" => array:7 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "KISHORE Nishant"
            ]
            2 => array:1 [
              "name" => "TAYLOR Aimee R"
            ]
            3 => array:1 [
              "name" => "VEMBAR Navin"
            ]
            4 => array:1 [
              "name" => "COHEN Ted"
            ]
            5 => array:1 [
              "name" => "BUCKEE Caroline O"
            ]
            6 => array:1 [
              "name" => "MENZIES Nicolas A"
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2023-08-30 11:42:22"
          "publicationUrl" => "https://www.sciencedirect.com/science/article/pii/S2589750021002144?via%3Dihub#!"
          "publicationInfo" => array:3 [
            "pages" => "e27-e36"
            "volume" => "4"
            "number" => "1"
          ]
          "type" => array:2 [
            "fr" => "Articles"
            "en" => "Journal articles"
          ]
          "support_type" => array:2 [
            "fr" => "Revue professionnelle"
            "en" => "Professional journal"
          ]
          "countries" => array:2 [
            "fr" => "Royaume-Uni"
            "en" => "United Kingdom"
          ]
          "abstract" => array:2 [
            "fr" => "In early 2020, the response to the SARS-CoV-2 pandemic focused on non-pharmaceutical interventions, some of which aimed to reduce transmission by changing mixing patterns between people. Aggregated location data from mobile phones are an important source of real-time information about human mobility on a population level, but the degree to which these mobility metrics capture the relevant contact patterns of individuals at risk of transmitting SARS-CoV-2 is not clear. In this study we describe changes in the relationship between mobile phone data and SARS-CoV-2 transmission in the USA."
            "en" => "In early 2020, the response to the SARS-CoV-2 pandemic focused on non-pharmaceutical interventions, some of which aimed to reduce transmission by changing mixing patterns between people. Aggregated location data from mobile phones are an important source of real-time information about human mobility on a population level, but the degree to which these mobility metrics capture the relevant contact patterns of individuals at risk of transmitting SARS-CoV-2 is not clear. In this study we describe changes in the relationship between mobile phone data and SARS-CoV-2 transmission in the USA."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      24 => Essec\Faculty\Model\Contribution {#2290
        #_index: "academ_contributions"
        #_id: "12708"
        #_source: array:18 [
          "id" => "12708"
          "slug" => "fast-approximation-of-the-sliced-wasserstein-distance-using-concentration-of-random-projections"
          "yearMonth" => "2021-12"
          "year" => "2021"
          "title" => "Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections"
          "description" => "NADJAHI, K., DURMUS, A., JACOB, P., BADEAU, R. et SIMSEKLI, U. (2021). Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections. Dans: <i>NeurIPS 2021 Thirty-fifth Conference on Neural Information Processing Systems</i>. Proceedings of Machine Learning Research."
          "authors" => array:5 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "NADJAHI Kimia"
            ]
            2 => array:1 [
              "name" => "DURMUS Alain"
            ]
            3 => array:1 [
              "name" => "BADEAU Roland"
            ]
            4 => array:1 [
              "name" => "SIMSEKLI Umut"
            ]
          ]
          "ouvrage" => "NeurIPS 2021 Thirty-fifth Conference on Neural Information Processing Systems"
          "keywords" => []
          "updatedAt" => "2023-09-22 17:04:38"
          "publicationUrl" => "https://arxiv.org/abs/2106.15427"
          "publicationInfo" => array:3 [
            "pages" => ""
            "volume" => ""
            "number" => ""
          ]
          "type" => array:2 [
            "fr" => "Actes d'une conférence"
            "en" => "Conference Proceedings"
          ]
          "support_type" => array:2 [
            "fr" => "Editeur"
            "en" => "Publisher"
          ]
          "countries" => array:2 [
            "fr" => "Royaume-Uni"
            "en" => "United Kingdom"
          ]
          "abstract" => array:2 [
            "fr" => ""
            "en" => ""
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      25 => Essec\Faculty\Model\Contribution {#2291
        #_index: "academ_contributions"
        #_id: "12750"
        #_source: array:18 [
          "id" => "12750"
          "slug" => "coupling-based-convergence-assessment-of-some-gibbs-samplers-for-high-dimensional-bayesian-regression-with-shrinkage-priors"
          "yearMonth" => "2022-07"
          "year" => "2022"
          "title" => "Coupling-based convergence assessment of some Gibbs samplers for high-dimensional Bayesian regression with shrinkage priors"
          "description" => "BISWAS, N., BHATTACHARYA, A., JACOB, P. et JOHNDROW, J. (2022). Coupling-based convergence assessment of some Gibbs samplers for high-dimensional Bayesian regression with shrinkage priors. <i>Journal of the Royal Statistical Society: Series B (Statistical Methodology)</i>, 84, pp. 973-996."
          "authors" => array:4 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "BISWAS Niloy"
            ]
            2 => array:1 [
              "name" => "BHATTACHARYA Anirban"
            ]
            3 => array:1 [
              "name" => "JOHNDROW James"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:5 [
            0 => "Bayesian inference"
            1 => "couplings"
            2 => "Gibbs sampling"
            3 => "Horseshoe prior"
            4 => "parallel computation"
          ]
          "updatedAt" => "2023-08-25 08:53:02"
          "publicationUrl" => "https://doi.org/10.1111/rssb.12495"
          "publicationInfo" => array:3 [
            "pages" => "973-996"
            "volume" => "84"
            "number" => ""
          ]
          "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" => "We consider Markov chain Monte Carlo (MCMC) algorithms for Bayesian high-dimensional regression with continuous shrinkage priors. A common challenge with these algorithms is the choice of the number of iterations to perform. This is critical when each iteration is expensive, as is the case when dealing with modern data sets, such as genome-wide association studies with thousands of rows and up to hundreds of thousands of columns. We develop coupling techniques tailored to the setting of high-dimensional regression with shrinkage priors, which enable practical, non-asymptotic diagnostics of convergence without relying on traceplots or long-run asymptotics. By establishing geometric drift and minorization conditions for the algorithm under consideration, we prove that the proposed couplings have finite expected meeting time. Focusing on a class of shrinkage priors which includes the ‘Horseshoe’, we empirically demonstrate the scalability of the proposed couplings. A highlight of our findings is that less than 1000 iterations can be enough for a Gibbs sampler to reach stationarity in a regression on 100,000 covariates. The numerical results also illustrate the impact of the prior on the computational efficiency of the coupling, and suggest the use of priors where the local precisions are Half-t distributed with degree of freedom larger than one."
            "en" => "We consider Markov chain Monte Carlo (MCMC) algorithms for Bayesian high-dimensional regression with continuous shrinkage priors. A common challenge with these algorithms is the choice of the number of iterations to perform. This is critical when each iteration is expensive, as is the case when dealing with modern data sets, such as genome-wide association studies with thousands of rows and up to hundreds of thousands of columns. We develop coupling techniques tailored to the setting of high-dimensional regression with shrinkage priors, which enable practical, non-asymptotic diagnostics of convergence without relying on traceplots or long-run asymptotics. By establishing geometric drift and minorization conditions for the algorithm under consideration, we prove that the proposed couplings have finite expected meeting time. Focusing on a class of shrinkage priors which includes the ‘Horseshoe’, we empirically demonstrate the scalability of the proposed couplings. A highlight of our findings is that less than 1000 iterations can be enough for a Gibbs sampler to reach stationarity in a regression on 100,000 covariates. The numerical results also illustrate the impact of the prior on the computational efficiency of the coupling, and suggest the use of priors where the local precisions are Half-t distributed with degree of freedom larger than one."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      26 => Essec\Faculty\Model\Contribution {#2292
        #_index: "academ_contributions"
        #_id: "13049"
        #_source: array:18 [
          "id" => "13049"
          "slug" => "an-invitation-to-sequential-monte-carlo-samplers"
          "yearMonth" => "2022-07"
          "year" => "2022"
          "title" => "An invitation to sequential Monte Carlo samplers"
          "description" => "DAI, C., HENG, J., JACOB, P. et WHITELEY, N. (2022). An invitation to sequential Monte Carlo samplers. <i>Journal of the American Statistical Association</i>, 117(539), pp. 1587-1600."
          "authors" => array:4 [
            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" => "DAI Chenguang"
            ]
            3 => array:1 [
              "name" => "WHITELEY Nick"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:4 [
            0 => "Monte Carlo methods"
            1 => "sequential inference"
            2 => "normalizing constant"
            3 => "interacting particle systems"
          ]
          "updatedAt" => "2023-07-18 16:16:04"
          "publicationUrl" => "https://www.tandfonline.com/doi/full/10.1080/01621459.2022.2087659"
          "publicationInfo" => array:3 [
            "pages" => "1587-1600"
            "volume" => "117"
            "number" => "539"
          ]
          "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" => "Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to approximate distributions of interest and their normalizing constants. These samplers originate from particle filtering for state space models and have become general and scalable sampling techniques. This article describes sequential Monte Carlo samplers and their possible implementations, arguing that they remain under-used in statistics, despite their ability to perform sequential inference and to leverage parallel processing resources among other potential benefits."
            "en" => "Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to approximate distributions of interest and their normalizing constants. These samplers originate from particle filtering for state space models and have become general and scalable sampling techniques. This article describes sequential Monte Carlo samplers and their possible implementations, arguing that they remain under-used in statistics, despite their ability to perform sequential inference and to leverage parallel processing resources among other potential benefits."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      27 => Essec\Faculty\Model\Contribution {#2293
        #_index: "academ_contributions"
        #_id: "13219"
        #_source: array:18 [
          "id" => "13219"
          "slug" => "unbiased-hamiltonian-monte-carlo-with-couplings"
          "yearMonth" => "2019-06"
          "year" => "2019"
          "title" => "Unbiased Hamiltonian Monte Carlo with couplings"
          "description" => "HENG, J. et JACOB, P. (2019). Unbiased Hamiltonian Monte Carlo with couplings. Biometrika."
          "authors" => array:2 [
            0 => array:3 [
              "name" => "HENG Jeremy"
              "bid" => "B00760223"
              "slug" => "heng-jeremy"
            ]
            1 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:4 [
            0 => "Coupling"
            1 => "Hamiltonian Monte Carlo method"
            2 => "Parallel computing"
            3 => "Unbiased estimation"
          ]
          "updatedAt" => "2024-10-31 13:51:19"
          "publicationUrl" => "https://doi.org/10.1093/biomet/asy074"
          "publicationInfo" => array:3 [
            "pages" => "769"
            "volume" => "106"
            "number" => "2"
          ]
          "type" => array:2 [
            "fr" => "Autre"
            "en" => "Other"
          ]
          "support_type" => array:2 [
            "fr" => "Revue scientifique"
            "en" => "Scientific journal"
          ]
          "countries" => array:2 [
            "fr" => null
            "en" => null
          ]
          "abstract" => array:2 [
            "fr" => "This is a correction to: Biometrika, Volume 106, Issue 2, June 2019, Pages 287–302, https://doi.org/10.1093/biomet/asy074."
            "en" => "This is a correction to: Biometrika, Volume 106, Issue 2, June 2019, Pages 287–302, https://doi.org/10.1093/biomet/asy074."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      28 => Essec\Faculty\Model\Contribution {#2294
        #_index: "academ_contributions"
        #_id: "13569"
        #_source: array:18 [
          "id" => "13569"
          "slug" => "metric-learning-with-horde-high-order-regularizer-for-deep-embeddings"
          "yearMonth" => "2019-10"
          "year" => "2019"
          "title" => "Metric Learning With HORDE: High-Order Regularizer for Deep Embeddings"
          "description" => "JACOB, P. et KLEIN, E. (2019). Metric Learning With HORDE: High-Order Regularizer for Deep Embeddings. Dans: 2019 IEEE/CVF International Conference on Computer Vision (ICCV)."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "bid" => "B00778976"
            ]
            2 => array:1 [
              "name" => "KLEIN Edouard"
            ]
          ]
          "ouvrage" => "2019 IEEE/CVF International Conference on Computer Vision (ICCV)"
          "keywords" => []
          "updatedAt" => "2023-01-20 10:18:35"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => "6538-6547"
            "volume" => ""
            "number" => ""
          ]
          "type" => array:2 [
            "fr" => "Communications dans une conférence"
            "en" => "Presentations at an Academic or Professional conference"
          ]
          "support_type" => array:2 [
            "fr" => "Editeur"
            "en" => "Publisher"
          ]
          "countries" => array:2 [
            "fr" => null
            "en" => null
          ]
          "abstract" => array:2 [
            "fr" => ""
            "en" => ""
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      29 => Essec\Faculty\Model\Contribution {#2295
        #_index: "academ_contributions"
        #_id: "13570"
        #_source: array:18 [
          "id" => "13570"
          "slug" => "diablo-dictionary-based-attention-block-for-deep-metric-learning"
          "yearMonth" => "2020-07"
          "year" => "2020"
          "title" => "DIABLO: Dictionary-based attention block for deep metric learning"
          "description" => "JACOB, P., PICARD, D., HISTACE, A. et KLEIN, E. (2020). DIABLO: Dictionary-based attention block for deep metric learning. <i>Pattern Recognition Letters</i>, 135, pp. 99-105."
          "authors" => array:5 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "bid" => "B00778976"
            ]
            2 => array:1 [
              "name" => "PICARD David"
            ]
            3 => array:1 [
              "name" => "HISTACE Aymeric"
            ]
            4 => array:1 [
              "name" => "KLEIN Edouard"
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2024-08-22 14:23:10"
          "publicationUrl" => "https://doi.org/10.1016/j.patrec.2020.03.020"
          "publicationInfo" => array:3 [
            "pages" => "99-105"
            "volume" => "135"
            "number" => ""
          ]
          "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" => "Recent breakthroughs in representation learning of unseen classes and examples have been made in deep metric learning by training at the same time the image representations and a corresponding metric with deep networks. Recent contributions mostly address the training part (loss functions, sampling strategies, etc.), while a few works focus on improving the discriminative power of the image representation. In this paper, we propose DIABLO, a dictionary-based attention method for image embedding. DIABLO produces richer representations by aggregating only visually-related features together while being easier to train than other attention-based methods in deep metric learning. This is experimentally confirmed on four deep metric learning datasets (Cub-200-2011, Cars-196, Stanford Online Products, and In-Shop Clothes Retrieval) for which DIABLO shows state-of-the-art performances."
            "en" => "Recent breakthroughs in representation learning of unseen classes and examples have been made in deep metric learning by training at the same time the image representations and a corresponding metric with deep networks. Recent contributions mostly address the training part (loss functions, sampling strategies, etc.), while a few works focus on improving the discriminative power of the image representation. In this paper, we propose DIABLO, a dictionary-based attention method for image embedding. DIABLO produces richer representations by aggregating only visually-related features together while being easier to train than other attention-based methods in deep metric learning. This is experimentally confirmed on four deep metric learning datasets (Cub-200-2011, Cars-196, Stanford Online Products, and In-Shop Clothes Retrieval) for which DIABLO shows state-of-the-art performances."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      30 => Essec\Faculty\Model\Contribution {#2296
        #_index: "academ_contributions"
        #_id: "13571"
        #_source: array:18 [
          "id" => "13571"
          "slug" => "efficient-codebook-and-factorization-for-second-order-representation-learning"
          "yearMonth" => "2019-09"
          "year" => "2019"
          "title" => "Efficient Codebook and Factorization for Second Order Representation Learning"
          "description" => "JACOB, P. et KLEIN, E. (2019). Efficient Codebook and Factorization for Second Order Representation Learning. Dans: 2019 IEEE International Conference on Image Processing (ICIP)."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "bid" => "B00778976"
            ]
            2 => array:1 [
              "name" => "KLEIN Edouard"
            ]
          ]
          "ouvrage" => "2019 IEEE International Conference on Image Processing (ICIP)"
          "keywords" => []
          "updatedAt" => "2023-01-20 10:39:51"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => "849-853"
            "volume" => ""
            "number" => ""
          ]
          "type" => array:2 [
            "fr" => "Communications dans une conférence"
            "en" => "Presentations at an Academic or Professional conference"
          ]
          "support_type" => array:2 [
            "fr" => "Editeur"
            "en" => "Publisher"
          ]
          "countries" => array:2 [
            "fr" => null
            "en" => null
          ]
          "abstract" => array:2 [
            "fr" => ""
            "en" => ""
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      31 => Essec\Faculty\Model\Contribution {#2297
        #_index: "academ_contributions"
        #_id: "13572"
        #_source: array:18 [
          "id" => "13572"
          "slug" => "leveraging-implicit-spatial-information-in-global-features-for-image-retrieval"
          "yearMonth" => "2018-10"
          "year" => "2018"
          "title" => "Leveraging Implicit Spatial Information in Global Features for Image Retrieval"
          "description" => "JACOB, P. et KLEIN, E. (2018). Leveraging Implicit Spatial Information in Global Features for Image Retrieval. Dans: 2018 25th IEEE International Conference on Image Processing (ICIP). Athens."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "bid" => "B00778976"
            ]
            2 => array:1 [
              "name" => "KLEIN Edouard"
            ]
          ]
          "ouvrage" => "2018 25th IEEE International Conference on Image Processing (ICIP)"
          "keywords" => []
          "updatedAt" => "2023-01-20 10:41:44"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => "2002-2006"
            "volume" => ""
            "number" => ""
          ]
          "type" => array:2 [
            "fr" => "Communications dans une conférence"
            "en" => "Presentations at an Academic or Professional conference"
          ]
          "support_type" => array:2 [
            "fr" => "Editeur"
            "en" => "Publisher"
          ]
          "countries" => array:2 [
            "fr" => null
            "en" => null
          ]
          "abstract" => array:2 [
            "fr" => ""
            "en" => ""
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      32 => Essec\Faculty\Model\Contribution {#2298
        #_index: "academ_contributions"
        #_id: "13993"
        #_source: array:18 [
          "id" => "13993"
          "slug" => "solving-the-poisson-equation-using-coupled-markov-chains"
          "yearMonth" => "2022-10"
          "year" => "2022"
          "title" => "Solving the Poisson equation using coupled Markov chains"
          "description" => "DOUC, R., JACOB, P., LEE, A. et VATS, D. (2022). Solving the Poisson equation using coupled Markov chains. Dans: Computational Methods for Unifying Multiple Statistical Analyses (Fusion). Luminy."
          "authors" => array:4 [
            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
            ]
            1 => array:1 [
              "name" => "DOUC Randal"
            ]
            2 => array:1 [
              "name" => "LEE Anthony"
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              "name" => "VATS Dootika"
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          "ouvrage" => "Computational Methods for Unifying Multiple Statistical Analyses (Fusion)"
          "keywords" => []
          "updatedAt" => "2023-05-24 01:00:41"
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            "fr" => ""
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            "en" => "Information Systems, Data Analytics and Operations"
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        ]
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      }
      33 => Essec\Faculty\Model\Contribution {#2299
        #_index: "academ_contributions"
        #_id: "14060"
        #_source: array:18 [
          "id" => "14060"
          "slug" => "rejoinder-a-gibbs-sampler-for-a-class-of-random-convex-polytopes"
          "yearMonth" => "2021-09"
          "year" => "2021"
          "title" => "Rejoinder—A Gibbs Sampler for a Class of Random Convex Polytopes"
          "description" => "JACOB, P., GONG, R., EDLEFSEN, P.T. et DEMPSTER, A.P. (2021). Rejoinder—A Gibbs Sampler for a Class of Random Convex Polytopes. <i>Journal of the American Statistical Association</i>, 116(535), pp. 1211-1214."
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            0 => array:3 [
              "name" => "JACOB Pierre"
              "bid" => "B00795650"
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              "name" => "GONG Ruobin"
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            2 => array:1 [
              "name" => "EDLEFSEN Paul T."
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            3 => array:1 [
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          "updatedAt" => "2023-06-26 11:34:02"
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            "fr" => "We are very grateful to all commenters for their stimulating remarks, questions, as well as useful pointers to the literature which span a wide range of statistical methods over decades of research. We have neither the space nor the knowledge to answer many of the questions raised, and we only aim to offer some clarifications. We hope that readers will be as enthusiastic as ourselves about research on the topics discussed by the commenters. In the following, we refer to Diaconis and Wang as DW, Hoffman, Hannig and Zhang as HHZ, Lawrence and Vander Wiel as LV, Ruggeri as R, Shafer as S, and Williams as W."
            "en" => "We are very grateful to all commenters for their stimulating remarks, questions, as well as useful pointers to the literature which span a wide range of statistical methods over decades of research. We have neither the space nor the knowledge to answer many of the questions raised, and we only aim to offer some clarifications. We hope that readers will be as enthusiastic as ourselves about research on the topics discussed by the commenters. In the following, we refer to Diaconis and Wang as DW, Hoffman, Hannig and Zhang as HHZ, Lawrence and Vander Wiel as LV, Ruggeri as R, Shafer as S, and Williams as W."
          ]
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            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
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        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      34 => Essec\Faculty\Model\Contribution {#2300
        #_index: "academ_contributions"
        #_id: "14214"
        #_source: array:18 [
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          "slug" => "artificial-intelligence-data-challenges"
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          "year" => "2022"
          "title" => "Artificial Intelligence, Data challenges"
          "description" => "NIANQUIOA, J., HENG, J. et JACOB, P. (2022). Artificial Intelligence, Data challenges. Dans: 2022 Institute of Mathematical Statistics (IMS) Annual Meeting in Probability and Statistics. London."
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              "name" => "HENG Jeremy"
              "bid" => "B00760223"
              "slug" => "heng-jeremy"
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              "name" => "JACOB Pierre"
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              "name" => "NIANQUIOA J."
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          "ouvrage" => "2022 Institute of Mathematical Statistics (IMS) Annual Meeting in Probability and Statistics"
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            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
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          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
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        +"_type": "_doc"
        +"_score": 5.708059
        +"parent": null
      }
      35 => Essec\Faculty\Model\Contribution {#2301
        #_index: "academ_contributions"
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          "title" => "Statistical Inference for Individual-based Models of Disease Transmission"
          "description" => "JU, N., HENG, J. et JACOB, P. (2024). Statistical Inference for Individual-based Models of Disease Transmission. Dans: 2024 Eastern Asia Chapter International Society for Bayesian Analysis Conference. Hong Kong."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "HENG Jeremy"
              "bid" => "B00760223"
              "slug" => "heng-jeremy"
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              "name" => "JACOB Pierre"
              "bid" => "B00795650"
              "slug" => "jacob-pierre"
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            2 => array:1 [
              "name" => "JU Nianqiao"
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          "ouvrage" => "2024 Eastern Asia Chapter International Society for Bayesian Analysis Conference"
          "keywords" => []
          "updatedAt" => "2024-07-16 19:21:55"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => ""
            "volume" => ""
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            "fr" => ""
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            "en" => "Information Systems, Data Analytics and Operations"
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          "indexedAt" => "2024-12-22T02:21:49.000Z"
        ]
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        +"_score": 5.708059
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    "docTitle" => "Pierre JACOB"
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