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

Doctoral Dissertations and Habilitation Theses

  • 11A. Lepoutre.

    Detection and tracking in Track-Before-Detect context using particle filtering, Université de Rennes 1, October 2016.


Articles in International Peer-Reviewed Journals

  • 12J.-D. Albert, V. Monbet, A. Jolivet-Gougeon, N. Fatih, M. Le Corvec, M. Seck, F. Charpentier, G. Coiffier, C. Boussard-Plédel, B. Bureau, P. Guggenbuhl, O. Loréal.

    A novel method for a fast diagnosis of septic arthritis using mid infrared and deported spectroscopy, in: Joint Bone Spine, 2016, vol. 83, no 3, pp. 318-323. [ DOI : 10.1016/j.jbspin.2015.05.009 ]

  • 13J. Bessac, P. Ailliot, J. Cattiaux, V. Monbet.

    Comparison of hidden and observed regime-switching autoregressive models for (u,v)-components of wind fields in the Northeast Atlantic, in: Advances in Statistical Climatology, Meteorology and Oceanography, 2016, vol. 2, no 1, pp. 1-16. [ DOI : 10.5194/ascmo-2-1-2016 ]

  • 14P. Bui Quang, C. Musso, F. Le Gland.

    Particle filtering and the Laplace method for target tracking, in: IEEE Transactions on Aerospace and Electronic Systems, February 2016, vol. 52, no 1, pp. 350-366. [ DOI : 10.1109/TAES.2015.140419 ]

  • 15F. Cérou, A. Guyader.

    Fluctuation analysis of adaptive multilevel splitting, in: The Annals of Applied Probability : an official journal of the institute of mathematical statistics, December 2016, vol. 26, no 6, pp. 3319-3380. [ DOI : 10.1214/16-AAP1177 ]

  • 16P. Héas, A. Drémeau, C. Herzet.

    An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model, in: SIAM Journal on Imaging Sciences, 2016, vol. 9, no 2, pp. 537-572, 37 pages. [ DOI : 10.1137/15M1023956 ]

  • 17D. Jacquemart, J. Morio.

    Adaptive interacting particle system algorithm for aircraft conflict probability estimation, in: Aerospace Science and Technology, June 2016, vol. 55, pp. 431-438. [ DOI : 10.1016/j.ast.2016.05.027 ]

  • 18M. Le Corvec, F. Charpentier, A. Kachenoura, S. Bensaid, S. Henno, E. Bardou-Jacquet, B. Turlin, V. Monbet, L. Senhadji, O. Loréal, O. Sire, J. F. Betagne, H. Tariel, F. Lainé.

    Fast and Non-Invasive Medical Diagnostic Using Mid Infrared Sensor: The AMNIFIR Project, in: IRBM, 2016, vol. 37, no 2, pp. 116-123. [ DOI : 10.1016/j.irbm.2016.03.003 ]

  • 19A. Lepoutre, O. Rabaste, F. Le Gland.

    Multitarget likelihood for Track-Before-Detect applications with amplitude fluctuations, in: IEEE Transactions on Aerospace and Electronic Systems, June 2016, vol. 52, no 3, pp. 1089-1107. [ DOI : 10.1109/TAES.2016.140909 ]

  • 20V. 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 ]


Articles in National Peer-Reviewed Journals

  • 21J.-D. Albert, V. Monbet, A. Jolivet-Gougeon, N. Fatih, M. Le Corvec, M. Seck, F. Charpentier, G. Coiffier, C. Boussard-Plédel, B. Bureau, P. Guggenbuhl, O. Loréal.

    Une nouvelle méthode pour le diagnostic rapide d'arthrite septique utilisant la spectroscopie infrarouge, in: Revue du Rhumatisme, 2016, vol. 83, pp. 295–300. [ DOI : 10.1016/j.rhum.2016.05.007 ]


International Conferences with Proceedings

  • 22P. Héas, C. Herzet.

    Reduced-Order Modeling of Hidden Dynamics, in: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASPP), Shangai, China, March 2016, pp. 1268–1272. [ DOI : 10.1109/ICASSP.2016.7471880 ]

  • 23K. Zoubert-Ousseni, C. Villien, F. Le Gland.

    Comparison of post-processing algorithms for indoor navigation trajectories, in: Proceedings of the 7th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Alcala de Henares, Spain, IEEE, October 2016, ISBN: 978-1-5090-2425-4nternational Conference on Indoor Positioning and Indoor Navigation. [ DOI : 10.1109/IPIN.2016.7743590 ]


Other Publications

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

  • 29M. 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.

  • 30M. Bocquel.

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

  • 31O. Cappé, S. J. Godsill, É. Moulines.

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  • 34P. Del Moral.

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  • 36P. 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.

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  • 38E. B. Dynkin, R. J. Vanderbei.

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  • 39D. 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.

  • 40D. 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.

  • 41D. Frenkel, B. Smit.

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

  • 42D. 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.

  • 43P. Glasserman.

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

  • 44P. 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.

  • 45N. 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.

  • 46F. 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.

  • 47J. Houssineau.

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

  • 48M. 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.

  • 49G. Kitagawa.

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  • 50H. R. Künsch.

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

  • 51P. 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.

  • 52F. 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.

  • 53F. 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.

  • 54J. S. Liu.

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

  • 55R. 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.

  • 56B. Ristić, M. S. Arulampalam, N. J. Gordon.

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

  • 57R. 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.

  • 58C. J. Stone.

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

  • 59L. D. Stone, R. L. Streit, T. L. Corwin, K. L. Bell.

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

  • 60P. Tandéo, P. Ailliot, J. Ruiz, A. Hannart, B. Chapron, A. Cuzol, V. Monbet, R. Easton, R. Fablet.

    Combining analog method and ensemble data assimilation: application to the Lorenz–63 chaotic system, in: Machine Learning and Data Mining Approaches to Climate Science, Cham, V. Lakshmanan, E. Gilleland, A. McGovern, M. Tingley (editors), Springer–Verlag, 2015, chap. 1, pp. 3–12.

  • 61S. Thrun, W. Burgard, D. Fox.

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

  • 62B.–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.

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