Major publications by the team in recent years
  • 1G. Biau, F. Cérou, A. Guyader.

    On the rate of convergence of the bagged nearest neighbor estimate, in: Journal of Machine Learning Research, February 2010, vol. 11, pp. 687–712.

  • 2G. Biau, F. Cérou, A. Guyader.

    On the rate of convergence of the functional k–nearest neighbor estimates, in: IEEE Transactions on Information Theory, April 2010, vol. IT–56, no 4, pp. 2034–2040.

  • 3G. Biau, F. Cérou, A. Guyader.

    New insights into approximate Bayesian computation, in: Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, February 2015, vol. 51, no 1, pp. 376–403.

  • 4F. Cérou, A. Guyader.

    Nearest neighbor classification in infinite dimension, in: ESAIM : Probability and Statistics, 2006, vol. 10, pp. 340–355.

  • 5F. Cérou, A. Guyader.

    Adaptive multilevel splitting for rare event analysis, in: Stochastic Analysis and Applications, March 2007, vol. 25, no 2, pp. 417–443.

  • 6F. Cérou, P. Del Moral, F. Le Gland, P. Lezaud.

    Genetic genealogical models in rare event analysis, in: ALEA, Latin American Journal of Probability and Mathematical Statistics, 2006, vol. 1, pp. 181–203, Paper 01–08.
  • 7A. Guyader, N. Hengartner, É. Matzner–Løber.

    Simulation and estimation of extreme quantiles and extreme probabilities, in: Applied Mathematics & Optimization, October 2011, vol. 64, no 2, pp. 171–196.

  • 8F. Le Gland, V. Monbet, V.–D. Tran.

    Large sample asymptotics for the ensemble Kalman filter, in: The Oxford Handbook of Nonlinear Filtering, Oxford, D. O. Crisan, B. L. Rozovskii (editors), Oxford University Press, 2011, chap. 22, pp. 598–631.
  • 9F. Le Gland, N. Oudjane.

    A sequential algorithm that keeps the particle system alive, in: Stochastic Hybrid Systems : Theory and Safety Critical Applications, Berlin, H. A. P. Blom, J. Lygeros (editors), Lecture Notes in Control and Information Sciences, Springer–Verlag, 2006, no 337, pp. 351–389.

  • 10C. Musso, N. Oudjane, F. Le Gland.

    Improving regularized particle filters, in: Sequential Monte Carlo Methods in Practice, New York, A. Doucet, N. de Freitas, N. J. Gordon (editors), Statistics for Engineering and Information Science, Springer–Verlag, 2001, chap. 12, pp. 247–271.

Publications of the year

Articles in International Peer-Reviewed Journals

  • 11C. Herzet, A. Drémeau, P. Héas.

    Model Reduction from Partial Observations, in: International Journal for Numerical Methods in Engineering, January 2018, vol. 113, no 3, pp. 479–511. [ DOI : 10.1002/nme.5623 ]

  • 12P. Héas, C. Herzet.

    Reduced Modeling of Unknown Trajectories, in: Archives of Computational Methods in Engineering, January 2018, vol. 25, no 1, pp. 87-101, https://arxiv.org/abs/1702.08846. [ DOI : 10.1007/s11831-017-9229-0 ]

  • 13M. Le Corvec, C. Jezequel, V. Monbet, N. Fatih, F. Charpentier, H. Tariel, C. Boussard-Plédel, B. Bureau, O. Loréal, O. Sire, E. Bardou-Jacquet.

    Mid-infrared spectroscopy of serum, a promising non-invasive method to assess prognosis in patients with ascites and cirrhosis, in: PLoS ONE, October 2017, vol. 12, no 10, e0185997 p. [ DOI : 10.1371/journal.pone.0185997 ]

  • 14V. Monbet, P. Ailliot.

    Sparse vector Markov switching autoregressive models. Application to multivariate time series of temperature, in: Computational Statistics and Data Analysis, 2017, vol. 108, pp. 40-51. [ DOI : 10.1016/j.csda.2016.10.023 ]


International Conferences with Proceedings

  • 15P. Héas, C. Herzet.

    Optimal Low-Rank Dynamic Mode Decomposition, in: Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing, New Orleans 2017, New Orleans, United States, March 2017, https://arxiv.org/abs/1701.01064. [ DOI : 10.1109/ICASSP.2017.7952999 ]

  • 16S. Sen, A. Crinière, L. Mevel, F. Cérou, J. Dumoulin.

    Seismic induced damage detection through parallel estimation of force and parameter using improved interacting Particle-Kalman filter, in: 11th International Workshop on Structural Health Monitoring, San Francisco, United States, September 2017.

  • 17K. Zoubert-Ousseni, C. Villien, F. Le Gland.

    Post-processing optimization of piecewise indoor trajectories based on IMU and RSS measurements, in: Proceedings of the 8th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, Japan, IEEE, September 2017. [ DOI : 10.1109/IPIN.2017.8115903 ]


Conferences without Proceedings

  • 18S. Sen, A. Crinière, L. Mevel, F. Cérou, J. Dumoulin.

    Estimation of time varying system parameters from ambient response using improved Particle-Kalman filter with correlated noise, in: EGU General Assembly 2017, Vienne, Austria, April 2017.


Other Publications

References in notes
  • 22A. Doucet, N. de Freitas, N. J. Gordon (editors)

    Sequential Monte Carlo methods in practice, Statistics for Engineering and Information Science, Springer–Verlag, New York, 2001.

  • 23M. S. Arulampalam, S. Maksell, N. J. Gordon, T. Clapp.

    A tutorial on particle filters for online nonlinear / non–Gaussian Bayesian tracking, in: IEEE Transactions on Signal Processing, February 2002, vol. SP–50, no 2 (Special issue on Monte Carlo Methods for Statistical Signal Processing), pp. 174–188.

  • 24M. Bocquel.

    Random finite sets in multi–target tracking: efficient sequential MCMC implementation, Department of Applied Mathematics, University of Twente, Enschede, October 2013.

  • 25O. Cappé, S. J. Godsill, É. Moulines.

    An overview of existing methods and recent advances in sequential Monte Carlo, in: Proceedings of the IEEE, May 2007, vol. 95, no 5 (Special issue on Large–Scale Dynamic Systems), pp. 899–924.

  • 26D. J. Daley, J. Gani.

    Epidemic modelling: An introduction, Cambridge Studies in Mathematical Biology, Cambridge University Press, Cambridge, 1999, vol. 15.

  • 27P.–T. De Boer, D. P. Kroese, S. Mannor, R. Y. Rubinstein.

    A tutorial on the cross–entropy method, in: Annals of Operations Research, January 2005, vol. 134 (Special issue on the Cross-Entropy Method for Combinatorial Optimization, Rare Event Simulation and Neural Computation), no 1, pp. 19–67.

  • 28P. Del Moral.

    Mean field simulation for Monte Carlo integration, Monographs on Statistics and Applied Probability, Chapman & Hall / CRC Press, London, 2013, vol. 126.

  • 29P. Del Moral.

    Feynman–Kac formulae. Genealogical and interacting particle systems with applications, Probability and its Applications, Springer–Verlag, New York, 2004.

  • 30P. Del Moral, L. Miclo.

    Branching and interacting particle systems approximations of Feynman–Kac formulae with applications to nonlinear filtering, in: Séminaire de Probabilités XXXIV, Berlin, J. Azéma, M. Émery, M. Ledoux, M. Yor (editors), Lecture Notes in Mathematics, Springer–Verlag, 2000, vol. 1729, pp. 1–145.

  • 31R. Douc, É. Moulines.

    Limit theorems for weighted samples with applications to sequential Monte Carlo methods, in: The Annals of Statistics, October 2008, vol. 36, no 5, pp. 2344–2376.

  • 32D. Fox, J. Hightower, L. Liao, D. Schulz, G. Borriello.

    Bayesian filtering for location estimation, in: IEEE Pervasive Computing, July/September 2003, vol. 2, no 3, pp. 24–33.

  • 33D. Fox, S. Thrun, W. Burgard, F. Dellaert.

    Particle filters for mobile robot localization, in: Sequential Monte Carlo Methods in Practice, New York, A. Doucet, N. de Freitas, N. J. Gordon (editors), Statistics for Engineering and Information Science, Springer–Verlag, 2001, chap. 19, pp. 401–428.

  • 34D. Frenkel, B. Smit.

    Understanding molecular simulation. From algorithms to applications, Computational Science Series, 2nd, Academic Press, San Diego, 2002, vol. 1.

  • 35D. T. Gillespie.

    Approximate accelerated stochastic simulation of chemically reacting systems, in: The Journal of Chemical Physics, July 2001, vol. 115, no 4, pp. 1716–1733.

  • 36P. Glasserman.

    Monte Carlo methods in financial engineering, Applications of Mathematics, Springer–Verlag, New York, 2004, vol. 53.

  • 37P. Glasserman, P. Heidelberger, P. Shahabuddin, T. Zajic.

    Multilevel splitting for estimating rare event probabilities, in: Operations Research, July–August 1999, vol. 47, no 4, pp. 585–600.

  • 38N. J. Gordon, D. J. Salmond, A. F. M. Smith.

    Novel approach to nonlinear / non–Gaussian Bayesian state estimation, in: IEE Proceedings, Part F, April 1993, vol. 140, no 2, pp. 107–113.

  • 39F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson, P.–J. Nordlund.

    Particle filters for positioning, navigation, and tracking, in: IEEE Transactions on Signal Processing, February 2002, vol. SP–50, no 2 (Special issue on Monte Carlo Methods for Statistical Signal Processing), pp. 425–437.

  • 40J. Houssineau.

    Representation and estimation of stochastic populations, School of Engineering and Physical Sciences, Heriot–Watt University, Edinburgh, August 2015.

  • 41P. Héas, C. Herzet.

    Low–Rank Dynamic Mode Decomposition: Optimal Solution in Polynomial–Time, in: ArXiv e-prints, October 2016.

  • 42M. Isard, A. Blake.

    Condensation — Conditional density propagation for visual tracking, in: International Journal of Computer Vision, August 1998, vol. 29, no 1, pp. 5–28.

  • 43G. Kitagawa.

    Monte Carlo filter and smoother for non–Gaussian nonlinear state space models, in: Journal of Computational and Graphical Statistics, 1996, vol. 5, no 1, pp. 1–25.

  • 44H. R. Künsch.

    Recursive Monte Carlo filters : Algorithms and theoretical analysis, in: The Annals of Statistics, October 2005, vol. 33, no 5, pp. 1983–2021.

  • 45P. L'Écuyer, V. Demers, B. Tuffin.

    Rare events, splitting, and quasi–Monte Carlo, in: ACM Transactions on Modeling and Computer Simulation, April 2007, vol. 17, no 2 (Special issue honoring Perwez Shahabuddin), Article 9.

  • 46F. Le Gland, N. Oudjane.

    A robustification approach to stability and to uniform particle approximation of nonlinear filters : the example of pseudo-mixing signals, in: Stochastic Processes and their Applications, August 2003, vol. 106, no 2, pp. 279-316.

  • 47F. Le Gland, N. Oudjane.

    Stability and uniform approximation of nonlinear filters using the Hilbert metric, and application to particle filters, in: The Annals of Applied Probability, February 2004, vol. 14, no 1, pp. 144–187.

  • 48J. S. Liu.

    Monte Carlo strategies in scientific computing, Springer Series in Statistics, Springer–Verlag, New York, 2001.

  • 49H. Louvin, E. Dumonteil, T. Lelièvre, M. Rousset, C. M. Diop.

    Adaptive multilevel splitting for Monte Carlo particle transport, in: EPJ Web of Conferences, 2017, vol. 153. [ DOI : 10.1051/epjconf/201715306006 ]

  • 50R. P. S. Mahler.

    Multitarget Bayes filtering via first–order multitarget moments, in: IEEE Transactions on Aerospace and Electronic Systems, October 2003, vol. AES–39, no 4, pp. 1152–1178.

  • 51B. Ristić, M. S. Arulampalam, N. J. Gordon.

    Beyond the Kalman Filter : Particle Filters for Tracking Applications, Artech House, Norwood, MA, 2004.

  • 52R. Y. Rubinstein, D. P. Kroese.

    The cross–entropy method. A unified approach to combinatorial optimization, Monte Carlo simulation and machine learning, Information Science and Statistics, Springer–Verlag, New York, 2004.

  • 53C. J. Stone.

    Consistent nonparametric regression (with discussion), in: The Annals of Statistics, July 1977, vol. 5, no 4, pp. 595–645.

  • 54L. D. Stone, R. L. Streit, T. L. Corwin, K. L. Bell.

    Bayesian multiple target tracking, 2nd, Artech House, Norwood, MA, 2014.

  • 55S. Thrun, W. Burgard, D. Fox.

    Probabilistic robotics, Intelligent Robotics and Autonomous Agents, The MIT Press, Cambridge, MA, 2005.

  • 56B.–N. Vo, W.–K. Ma.

    The Gaussian mixture probability hypothesis density filter, in: IEEE Transactions on Signal Processing, November 2006, vol. SP–54, no 11, pp. 4091–4104.

  • 57K. Zoubert-Ousseni, C. Villien, F. Le Gland.

    Comparison of post-processing algorithms for indoor navigation trajectories, in: 7th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Alcala de Henares, Spain, IEEE, October 2016. [ DOI : 10.1109/IPIN.2016.7743590 ]

  • 58A. W. van der Vaart, J. A. Wellner.

    Weak convergence and empirical processes, Springer Series in Statistics, Springer–Verlag, Berlin, 1996.