Nicolas Bousquet

Seal measuring campaign - Canada 2008

Scientific Director at Quantmetry
Associate Professor at Sorbonne Université (Université Pierre-et-Marie Curie)

                                                                                      Team "Statistics, Data, Algorithms"

Desk (SU): 15-25.203
Presence (SU): Tuesday-Wednesday


nbousquet 'at '
nicolas.bousquet 'at'

Research Gate / LinkedIn
Main interests
:   Bayesian statistics, treatment of uncertainties and risk in industrial and ecological resource management, machine / deep learning, artificial intelligence

Janvier-Mars 2020

Cours M2 / ISUP Modélisation et Statistique Bayésienne Computationnelle (Sorbonne Université)
                              (Bayesian Modeling and Computational Statistics)

Research papers

      N. Benoumechiara, B. Michel, P. Saint-Pierre, N. Bousquet (2019). Detecting and modeling worst-case dependence structures between random inputs of computational reliability models

      N. Thiebaut, A. Simoulin, K. Neuberger, I. Ibnouhsein, N. Bousquet, N. Reix, S. Molière, C. Mathelin (2018). An innovative solution for breast cancer textual big data analysis  

      C. Sonigo, S. Jankowski, O. Yoo, O. Trassard, N. Bousquet, M. Grynberg, I. Beau, N. Binart (2018). High-throughput ovarian follicle counting by an innovative deep learning approach.
                                                                                        Scientific Reports, 8.     
      C. Sonigo, S. Jankowski, O. Yoo, O. Trassard, N. Bousquet, M. Grynberg, I. Beau, N. Binart (2018). Comptage des follicules primordiaux par deep learning : l'intelligence artificielle au service de
                                                                                        l'étude de la reproduction. Annales d'Endocrinologie, 79, 225.

      N. Bousquet, T. Klein, V. Moutoussamy (2018). Approximation of limit state surfaces in monotonic Monte Carlo settings, with applications to classification
                                                                                        SIAM Journal of Uncertainty Quantification
, 6: 1-33.
      N. Bousquet (2018). Modeling extreme events in energy companies. In: Statistics Reference Online (Wiley Stats Ref), DOI: 10.1002/9781118445112.stat08011

      S. Fu, M. Couplet, N. Bousquet (2017). An adaptive kriging method for solving nonlinear inverse statistical problems Environmetrics, 28 (4).
N. Pérot, N. Bousquet (2017). Functional Weibull-based models of steel fracture toughness for structural risk analysis: estimation and selection. Reliability Engineering and System Safety, 165: 355-367

      L.J. Wolfson, N. Bousquet (2016). Elicitation. In: Statistics Reference Online (Wiley Stats Ref), DOI: 10.1002/9781118445112.stat00231.pub2
      M. Keller, A.-L. Popelin, N. Bousquet, E. Remy (2015). Nonparametric estimation of the probability of detection of flaws in an industrial component, from destructive and nondestructive testing data,
                                                                                                   using Approximate Bayesian Computation. Risk Analysis, 35: 1595-1610
      A. Pasanisi, C. Roero E. Remy, N. Bousquet (2015). On the practical interest of discrete Inverse Polya and Weibull-1 models in industrial reliability studies.
                                                                                                         Quality and Reliability Engineering International
(in press).   
     S. Fu, G. Celeux, N. Bousquet, M. Couplet (2015). Bayesian inference for inverse problems occuring in uncertainty analysis.
                                                                                                                                           International Journal for Uncertainty Quantification, 5(1): 73-98
       N. Bousquet, M. Fouladirad, A. Grall, C. Paroissin (2015). Bayesian gamma processes for optimizing condition-based maintenance under uncertainty.
                                                                                                                                            Applied Stochastic Models in Business and Industry, 31(3): 360-379
      P. Lemaître, E. Sergienko, A. Arnaud, N. Bousquet,  F. Gamboa, B. Iooss (2015)  Density modification based reliability sensitivity indices.
                                                                                                                                                   Journal of Statistical Computation  and Simulation,
85: 1200-1223
     N. Bousquet (2012).  Accelerated Monte Carlo estimation of  exceedance probabilities under monotonicity constraints. Annales de la Faculté des Sciences de Toulouse, 21(3):  557-591 (arxiv)
      A. Pasanisi, S. Fu, N. Bousquet (2012). Estimating discrete Markov models from various incomplete data schemes. Computational Statistics & Data Analysis 56(9): 2609-2625

N. Bousquet (2010). Eliciting vague but proper maximal entropy priors in Bayesian experiments. Stat. Papers. 51: 613-62

     N. Bousquet  (2008). Diagnostics of prior-data agreement in applied Bayesian analysis. Journal of Applied Statistics, 35: 1011-1029
     H. Bertholon, N. Bousquet, G. Celeux (2006). An alternative competing risk model to the Weibull distribution in lifetime data analysis. Lifetime Data Analysis, 12:  481-504
     N. Bousquet (2005). Eliciting prior distributions for Weibull inference in an industrial context. Communications in Dependability and Quality Management, 8: 12-19.

    With a focus in ecology, fishery science and natural resource management 

     N. Bousquet, E. Chassot, E. Dortel, J. Million, A. Fonteneau, J.-P. Hallier (2017). A Bayesian Brownie-Petersen model for assessing the mortality and abundance of Indian Ocean tunas.
                                                                                                                                             Application to skipjack (Katswonus pelamis). In revision.
     E. Dortel, F. Sardenne, N. Bousquet, E. Rivot, J. Million, G. Le Croizier, E. Chassot  (2015). An integrated Bayesian modelling approach for the growth of Indian Ocean yellowfin tuna
                                                                                                      Fisheries Research, 163: 69-84
     N. Bousquet, E. Chassot, D. Duplisea, M. Hammill (2014). Forecasting the major influences of predation and environment on cod recovery in the  northern Gulf of St. Lawrence.
                                                                                                          PloS ONE
9(2): e82836     
     B. Archambault, O. Lepape, N. Bousquet, E. Rivot (2014). Density dependence can be revealed by modeling the variance in the stock-recruitment process: an application to flatfishes.
                                                                                                           ICES Journal of Marine Science, doi: 10.1093/icejms/fst203
     E. Dortel, F. Massot-Granier, E. Rivot, J. Million, J.-P. Hallier, E. Morize, J.-M. Munaron, N. Bousquet,  E. Chassot (2013). Accounting for Age Uncertainty in Growth Modeling, the
                                                                                                              Case Study of Yellowfin Tuna (Thunnus albacares) of the Indian Ocean. PloS ONE 8(4): e60886          
      N. Bousquet,  N. Cadigan, T. Duchesne, L.-P. Rivest (2010). Detecting and correcting underreported catches in fish stock assessment : trial of a new method. Canadian Journal
                                                                                                              of Fisheries and Aquatic Sciences, 67(8): 1247-1261
      E. Chassot, D. Duplisea, M. Hammill, A. Caskenette, N. Bousquet, Y. Lambert, G. Stenson (2009). The role of predation by harp seals (Phoca groenlandica) in the collapse and
                                                                                                            non-recovery of northen Gulf of St. Lawrence cod (Gadus morhua). Marine Ecology Progress Series379: 279-297      
     N. Bousquet, T. Duchesne, L.-P. Rivest (2008). Redefining the maximum sustainable yield for the Schaefer population model including multiplicative environmental noise
                                                                                                           Journal of Theoretical Biology, 254: 65-7


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