المؤلفون: Emmanuel Vazquez, Souleymane Zio, Claire Cannamela, Guillaume Perrin, Julien Bect

المساهمون: Laboratoire des signaux et systèmes (L2S), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Méthodes d'Analyse Stochastique des Codes et Traitements Numériques (GdR MASCOT-NUM), Centre National de la Recherche Scientifique (CNRS), Laboratoire de Détection et de Géophysique (CEA) (LDG), DAM Île-de-France (DAM/DIF), Direction des Applications Militaires (DAM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction des Applications Militaires (DAM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)

المصدر: 14th World Congress in Computational Mechanics and ECCOMAS Congress 2020 (WCCM-ECCOMAS)
14th World Congress in Computational Mechanics and ECCOMAS Congress 2020 (WCCM-ECCOMAS), Jan 2021, Virtual conference, originally scheduled in Paris, France. ⟨10.23967/wccm-eccomas.2020.260⟩

مصطلحات موضوعية: FOS: Computer and information sciences, Discretization, Computer science, FOS: Physical sciences, Gaussian processes, Richardson extrapolation, Quantification of uncertainty, Bayesian inference, Statistics - Applications, Methodology (stat.ME), [PHYS.PHYS.PHYS-COMP-PH]Physics [physics]/Physics [physics]/Computational Physics [physics.comp-ph], symbols.namesake, Applied mathematics, Applications (stat.AP), Gaussian process, Statistics - Methodology, Parametric statistics, [STAT.AP]Statistics [stat]/Applications [stat.AP], Partial differential equation, Finite difference, Grid convergence index, Computational Physics (physics.comp-ph), Physics - Medical Physics, Finite element method, Multi-fidelity, symbols, Medical Physics (physics.med-ph), Discretization uncertainty, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Physics - Computational Physics

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b06a44504e7e820871e2b105e219b212
https://doi.org/10.23967/wccm-eccomas.2020.260

المؤلفون: Emmanuel Vazquez, Séverine Demeyer, Julien Bect, Rémi Stroh, Damien Marquis, Nicolas Fischer

المساهمون: Laboratoire National de Métrologie et d'Essais [Trappes] (LNE ), Laboratoire des signaux et systèmes (L2S), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Méthodes d'Analyse Stochastique des Codes et Traitements Numériques (GdR MASCOT-NUM), Centre National de la Recherche Scientifique (CNRS), This work was supported by the French Association Nationale de la Recherche et de la Technologie (ANRT) under grant CIFRE 2014/1350., Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

المصدر: Technometrics
Technometrics, Taylor & Francis, In press, ⟨10.1080/00401706.2021.1935324⟩
Technometrics, Taylor & Francis, In press

مصطلحات موضوعية: FOS: Computer and information sciences, Statistics and Probability, Mathematical optimization, Computer science, media_common.quotation_subject, Bayesian probability, Computer experiments, 0211 other engineering and technologies, Fidelity, Machine Learning (stat.ML), 02 engineering and technology, Statistics - Applications, 01 natural sciences, Gaussian process emulator, Methodology (stat.ME), Reduction (complexity), 010104 statistics & probability, Surrogate model, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Statistics - Machine Learning, Applications (stat.AP), 0101 mathematics, Statistics - Methodology, Uncertainty reduction theory, media_common, Meta-model, [STAT.AP]Statistics [stat]/Applications [stat.AP], 021103 operations research, Sequential design of experiments, Applied Mathematics, Computer experiment, Stochastic simulator, Multi-fidelity, Sequential analysis, Modeling and Simulation, [STAT.ME]Statistics [stat]/Methodology [stat.ME]

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d40b8af82c9855cdea578b32a60ee47
https://hal-centralesupelec.archives-ouvertes.fr/hal-02902333v3/document

المؤلفون: François Bachoc, David Ginsbourger, Julien Bect

المساهمون: Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Méthodes d'Analyse Stochastique des Codes et Traitements Numériques (GdR MASCOT-NUM), Centre National de la Recherche Scientifique (CNRS), Institut de Mathématiques de Toulouse UMR5219 (IMT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), IDIAP Research Institute, Institute of Mathematical Statistics and Actuarial Science [Bern] (IMSV), University of Bern, Part of David Ginsbourger’s contribution took place within framework of the 'Bayesian set estimation under random field priors' project (Number 146354) funded by the Swiss National Science Foundation., Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)

المصدر: Bernoulli 25, no. 4A (2019), 2883-2919
Bernoulli
Bernoulli, 2019, 25 (4A), pp.2883-2919. ⟨10.3150/18-bej1074⟩
Bect, Julien; Bachoc, François; Ginsbourger, David (2019). A supermartingale approach to Gaussian process based sequential design of experiments. Bernoulli, 25(4A), pp. 2883-2919. International Statistical Institute 10.3150/18-BEJ1074 <http://dx.doi.org/10.3150/18-BEJ1074>
Bernoulli, Bernoulli Society for Mathematical Statistics and Probability, 2019, 25 (4A), pp.2883-2919. ⟨10.3150/18-bej1074⟩

مصطلحات موضوعية: FOS: Computer and information sciences, Statistics and Probability, Mathematical optimization, MSC 2010 subject classifications: Primary 60G15, 62L05, secondary 68T05, Machine Learning (stat.ML), Mathematics - Statistics Theory, Statistics Theory (math.ST), sequential design of experiments, 01 natural sciences, 010104 statistics & probability, symbols.namesake, 510 Mathematics, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 360 Social problems & social services, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Statistics - Machine Learning, uncertainty functional, active learning, Convergence (routing), FOS: Mathematics, 0101 mathematics, stepwise uncertainty reduction, Gaussian process, Global optimization, Mathematics, Sequence, convergence, Probability (math.PR), 010102 general mathematics, Function (mathematics), Computer experiment, [MATH.MATH-PR]Mathematics [math]/Probability [math.PR], Convergence of random variables, Sequential analysis, supermartingale, symbols, Mathematics - Probability

وصف الملف: application/pdf

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1255f9b95b1741f3344819836ebd1421
https://doi.org/10.3150/18-bej1074

المؤلفون: Nicolas Fischer, Emmanuel Vazquez, Séverine Demeyer, Julien Bect, Rémi Stroh, Damien Marquis

المساهمون: Laboratoire National de Métrologie et d'Essais [Trappes] (LNE ), Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Méthodes d'Analyse Stochastique des Codes et Traitements Numériques (GdR MASCOT-NUM), Centre National de la Recherche Scientifique (CNRS)

المصدر: Fire Safety Journal
Fire Safety Journal, Elsevier, 2017, 91, pp.1016-1025. ⟨10.1016/j.firesaf.2017.03.059⟩

مصطلحات موضوعية: Discretization, Computer science, Computer experiments, Bayesian probability, General Physics and Astronomy, 020101 civil engineering, 02 engineering and technology, computer.software_genre, Bayesian inference, 01 natural sciences, 0201 civil engineering, 010104 statistics & probability, Kriging, General Materials Science, 0101 mathematics, Safety, Risk, Reliability and Quality, Simulation, Risk assessment, Meta-model, [STAT.AP]Statistics [stat]/Applications [stat.AP], Statistics, General Chemistry, Solver, Computer experiment, Metamodeling, Bayesian statistics, Multi-fidelity, Data mining, [STAT.ME]Statistics [stat]/Methodology [stat.ME], computer

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9aa8d5efb61555f5a88f76d1b295ed21
https://doi.org/10.1016/j.firesaf.2017.03.059

المؤلفون: Paul Feliot, Julien Bect, Emmanuel Vazquez

المصدر: Springer, Journal of Global Optimization. 67(1):97-133

الإتاحة: https://ideas.repec.org/a/spr/jglopt/v67y2017i1d10.1007_s10898-016-0427-3.html

المؤلفون: Paul Feliot, Julien Bect, Emmanuel Vazquez

المساهمون: Safran Aircraft Engines, Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Méthodes d'Analyse Stochastique des Codes et Traitements Numériques (GdR MASCOT-NUM), Centre National de la Recherche Scientifique (CNRS), Giuseppe Nicosia, Panos Pardalos, Giovanni Giuffrida, Renato Umeton, Vincenzo Sciacca

المصدر: Machine Learning, Optimization, and Data Science ISBN: 9783030137083
LOD
Machine Learning, Optimization, and Data Science. 4th International Conference (LOD 2018)
Machine Learning, Optimization, and Data Science. 4th International Conference (LOD 2018), Sep 2018, Volterra, Italy. pp.533-544

مصطلحات موضوعية: Sequential Monte-Carlo, Importance sampling, Mathematical optimization, Generalization, Computer science, Bayesian probability, Mathematics - Statistics Theory, Statistics Theory (math.ST), 02 engineering and technology, 01 natural sciences, Measure (mathematics), Multi-objective optimization, 010104 statistics & probability, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, 0101 mathematics, Mathematics - Optimization and Control, Bayesian optimization, Lebesgue measure, User preferences, Optimization and Control (math.OC), 020201 artificial intelligence & image processing, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], Particle filter

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::00068b372c4c41e170e194eec40fffd2
https://doi.org/10.1007/978-3-030-13709-0_45

المؤلفون: Yann Richet, Clément Chevalier, Julien Bect, David Ginsbourger, Dario Azzimonti

المساهمون: Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), Università della Svizzera italiana = University of Italian Switzerland (USI)-Scuola universitaria professionale della Svizzera italiana [Manno] (SUPSI), IDIAP Research Institute, Institute of Mathematical Statistics and Actuarial Science [Bern] (IMSV), University of Bern, Université de Neuchâtel (UNINE), Laboratoire des signaux et systèmes (L2S), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Méthodes d'Analyse Stochastique des Codes et Traitements Numériques (GdR MASCOT-NUM), Centre National de la Recherche Scientifique (CNRS), Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PSE-ENV/SCAN, Idiap Research Institute (Idiap), Institut de Statistique [Neuchâtel] (UNINE), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

المصدر: INI workshop on "Key UQ methodologies and motivating applications" (UNQW01)
INI workshop on "Key UQ methodologies and motivating applications" (UNQW01), Jan 2018, Cambridge, United Kingdom
Technometrics
Technometrics, Taylor & Francis, 2021, 63 (1), pp.13-26. ⟨10.1080/00401706.2019.1693427⟩

مصطلحات موضوعية: Statistics and Probability, FOS: Computer and information sciences, Batch sequential strategies, Mathematical optimization, Gaussian process model, Computer science, 0211 other engineering and technologies, Machine Learning (stat.ML), Mathematics - Statistics Theory, 02 engineering and technology, Statistics Theory (math.ST), 01 natural sciences, Set (abstract data type), Methodology (stat.ME), 010104 statistics & probability, symbols.namesake, 510 Mathematics, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Statistics - Machine Learning, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], False positive paradox, FOS: Mathematics, 0101 mathematics, Adaptive design of experiments, Gaussian process, Uncertainty reduction theory, Statistics - Methodology, ComputingMilieux_MISCELLANEOUS, 021103 operations research, Applied Mathematics, Design of experiments, Excursion, Function (mathematics), Conservative estimates, Computer experiment, Excursion sets, Modeling and Simulation, symbols, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Stepwise Uncertainty Reduction, 360 Social problems & social services

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fcd09ad32031c1583415c205d1d086a8

المؤلفون: Emmanuel Vazquez, Nicolas Fischer, Séverine Demeyer, Julien Bect, Rémi Stroh

المساهمون: Laboratoire National de Métrologie et d'Essais [Trappes] (LNE ), Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Méthodes d'Analyse Stochastique des Codes et Traitements Numériques (GdR MASCOT-NUM), Centre National de la Recherche Scientifique (CNRS)

المصدر: 11th International Conference on Advanced Mathematical and Computational Tools in Metrology and Testing (AMTCM 2017)
11th International Conference on Advanced Mathematical and Computational Tools in Metrology and Testing (AMTCM 2017), Aug 2017, Glasgow, United Kingdom

مصطلحات موضوعية: FOS: Computer and information sciences, Mathematical optimization, Inverse-chi-squared distribution, Computer science, Posterior probability, Bayesian probability, Applied probability, Empirical probability, Bayesian inference, Machine learning, computer.software_genre, Statistics - Computation, Methodology (stat.ME), symbols.namesake, Bayesian hierarchical modeling, [STAT.CO]Statistics [stat]/Computation [stat.CO], Gaussian process, Statistics - Methodology, Computation (stat.CO), business.industry, Imprecise probability, Bayesian statistics, symbols, Artificial intelligence, business, Bayesian linear regression, computer, [STAT.ME]Statistics [stat]/Methodology [stat.ME]

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6fdc587a2e921ead5314ad2506d29fe1
https://hal-centralesupelec.archives-ouvertes.fr/hal-01590748/document

المؤلفون: Julien Bect, Alireza Roodaki, Gilles Fleury

المساهمون: Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Supélec Sciences des Systèmes (E3S), Ecole Supérieure d'Electricité - SUPELEC (FRANCE)

المصدر: IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2014, 62 (16), pp.4091-4104. ⟨10.1109/TSP.2014.2333569⟩

مصطلحات موضوعية: FOS: Computer and information sciences, Mathematical optimization, Signal decomposition, Stochastic EM, Bayesian inference, Monte Carlo method, Posterior probability, 02 engineering and technology, Statistics - Computation, 01 natural sciences, 010104 statistics & probability, symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, 0101 mathematics, Electrical and Electronic Engineering, [STAT.CO]Statistics [stat]/Computation [stat.CO], Divergence (statistics), Computation (stat.CO), Mathematics, Variable (mathematics), Label-switching, 020206 networking & telecommunications, Trans-dimensional MCMC, Statistics::Computation, Additive white Gaussian noise, Counting problem, Signal Processing, symbols, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithm, Curse of dimensionality

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3152799856d286c081b5d17fa6ee8055
https://doi.org/10.1109/tsp.2014.2333569

المؤلفون: Julien Bect, David Ginsbourger, Dario Azzimonti, Clément Chevalier

المساهمون: Institute of Mathematical Statistics and Actuarial Science [Bern] (IMSV), University of Bern, Méthodes d'Analyse Stochastique des Codes et Traitements Numériques (GdR MASCOT-NUM), Centre National de la Recherche Scientifique (CNRS), Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Institut de Mathématiques (UNINE), Université de Neuchâtel (UNINE), Idiap Research Institute (Idiap), Swiss National Science Foundation: Dario Filippo Azzimonti 146354

المصدر: Azzimonti, Dario Filippo; Bect, Julien; Chevalier, Clément; Ginsbourger, David (2016). Quantifying Uncertainties on Excursion Sets Under a Gaussian Random Field Prior. SIAM/ASA Journal on Uncertainty Quantification, 4(1), pp. 850-874. Society for Industrial and Applied Mathematics 10.1137/141000749 <http://dx.doi.org/10.1137/141000749>
SIAM/ASA Journal on Uncertainty Quantification
SIAM/ASA Journal on Uncertainty Quantification, ASA, American Statistical Association, 2016, 4 (1), pp.850-874. ⟨10.1137/141000749⟩
SIAM/ASA J. Uncertainty Quantification

مصطلحات موضوعية: FOS: Computer and information sciences, Statistics and Probability, Closed set, Computer science, Monte Carlo method, Posterior probability, 0211 other engineering and technologies, Gaussian processes, Machine Learning (stat.ML), Mathematics - Statistics Theory, Statistics Theory (math.ST), 02 engineering and technology, distance transform, Statistics - Computation, 01 natural sciences, Set estimation, Gaussian random field, conditional simulations, 010104 statistics & probability, symbols.namesake, 510 Mathematics, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Statistics - Machine Learning, 360 Social problems & social services, FOS: Mathematics, Discrete Mathematics and Combinatorics, 0101 mathematics, [STAT.CO]Statistics [stat]/Computation [stat.CO], Gaussian process, Computation (stat.CO), 021103 operations research, Applied Mathematics, Excursion, Modeling and Simulation, symbols, Statistics, Probability and Uncertainty, 62M30, 60G15, Algorithm, Realization (probability)

وصف الملف: application/pdf

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::60546ce4891beafbb8410dbaa17018d9
https://boris.unibe.ch/87839/2/1501.03659v2.pdf