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.

  • 3F. Cérou, A. Guyader.

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

  • 4F. Cérou, A. Guyader.

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

  • 5F. 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.
  • 6T. Furon, A. Guyader, F. Cérou.

    On the design and optimization of Tardos probabilistic fingerprinting codes, in: 10th International Workshop on Information Hiding, Santa Barbara, Berlin, K. Solanki, K. Sullivan, U. Madhow (editors), Lecture Notes in Computer Science, Springer, May 2008, vol. 5284, pp. 341–356.

  • 7F. Le Gland, L. Mevel.

    Exponential forgetting and geometric ergodicity in hidden Markov models, in: Mathematics of Control, Signals, and Systems, 2000, vol. 13, no 1, pp. 63–93.

  • 8F. 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.

  • 9C. 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

Articles in International Peer-Reviewed Journals

  • 11V. Caron, A. Guyader, M. Munoz Zuniga, B. Tuffin.

    Some Recent Results in Rare Event Estimation, in: ESAIM Proceedings, 2013.

  • 12F. Cérou, A. Guyader, T. Lelièvre, F. Malrieu.

    On the length of one-dimensional reactive paths, in: ALEA. Latin American Journal of Probability and Mathematical Statistics, 2013, vol. 10, no 1, pp. 359-389.

  • 13A. Guyader, N. Hengartner.

    On the Mutual Nearest Neighbors Estimate in Regression, in: Journal of Machine Learning Research, 2013, vol. 14, pp. 2361-2376.

  • 14A. Guyader, N. Jégou, A. B. Németh, S. Z. Németh.

    A geometrical approach to iterative isotone regression, in: Applied Mathematics and Computation, 2014, vol. 227, pp. 359-369. [ DOI : 10.1016/j.amc.2013.11.048 ]

  • 15D. Jacquemart, J. Morio.

    Conflict probability estimation between aircraft with dynamic importance splitting, in: Safety Science, 2013, vol. 51, no 1, pp. 94-100. [ DOI : 10.1016/j.ssci.2012.05.010 ]

  • 16J. Morio, R. Pastel, F. Le Gland.

    Missile target accuracy estimation with importance splitting, in: Aerospace Science and Technology, 2013, vol. 25, pp. 40-44. [ DOI : 10.1016/j.ast.2011.12.006 ]

  • 17R. Pastel, J. Morio, F. Le Gland.

    Improvement of satellite conflict prediction reliability through use of the adaptive splitting technique, in: Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, January 2014, vol. 228, no 1, pp. 77-85. [ DOI : 10.1177/0954410012467725 ]


International Conferences with Proceedings

  • 18A. Goswami, F. Le Gland.

    Marginalization for rare event simulation in switching diffusions, in: Proceedings of the International Conference on Mathematical Techniques in Engineering Applications, Dehradun, India, 2013.

  • 19D. Jacquemart, F. Le Gland, J. Morio.

    A combined importance splitting and sampling algorithm for rare event estimation, in: Proceedings of the 2013 Winter Simulation Conference, Washington DC, United States, R. Pasupathy, S.-H. Kim, A. Tolk, R. R. Hill, M. E. Kuhl (editors), 2013, pp. 1035-1046. [ DOI : 10.1109/WSC.2013.6721493 ]

  • 20A. Lepoutre, O. Rabaste, F. Le Gland.

    Exploiting amplitude spatial coherence for multi-target particle filter in track-before-detect, in: Proceedings of the 16th International Conference on Information Fusion, Istanbul, Turkey, 2013, pp. 319-326.


National Conferences with Proceedings

  • 21A. Lepoutre, O. Rabaste, F. Le Gland.

    Filtres particulaires en contexte track-before-detect en présence de fluctuations d'amplitude de type Swerling 1 et 3, in: Actes du 24ème Colloque sur le Traitement du Signal et des Images, Brest, France, GRETSI, 2013.


Other Publications

References in notes
  • 23A. 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.
  • 24P. Ailliot, C. Maisondieu, V. Monbet.

    Dynamical partitioning of directional ocean wave spectra, in: Probabilistic Engineering Mechanics, July 2013, vol. 33, pp. 95–102.

  • 25M. 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.
  • 26M. Baldé, U. Boscain, P. Mason.

    A note on stability conditions for planar switched systems, in: International Journal of Control, October 2009, vol. 82, no 10, pp. 1882–1888.
  • 27J.–B. Bardet, A. Christen, A. Guillin, F. Malrieu, P.–A. Zitt.

    Total variation estimates for the TCP process, in: Electronic Journal of Probability, 2013, vol. 18, no 10, pp. 1–21.

  • 28M. Benaïm, S. Le Borgne, F. Malrieu, P.–A. Zitt.

    On the stability of planar randomly switched systems, in: The Annals of Applied Probability, February 2014, vol. 24, no 1, pp. 292–311.

  • 29O. 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, pp. 899–924.
  • 30O. Cappé, É. Moulines, T. Rydén.

    Inference in hidden Markov models, Springer Series in Statistics, Springer–Verlag, New York, 2005.
  • 31P.–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.
  • 32P. Del Moral.

    Mean field simulation for Monte Carlo integration, Monographs on Statistics and Applied Probability, Chapman & Hall / CRC Press, London, 2013, vol. 126.
  • 33P. Del Moral.

    Feynman–Kac formulae. Genealogical and interacting particle systems with applications, Probability and its Applications, Springer–Verlag, New York, 2004.
  • 34P. Del Moral, A. Guionnet.

    On the stability of interacting processes with applications to filtering and genetic algorithms, in: Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, 2001, vol. 37, no 2, pp. 155–194.
  • 35P. 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.
  • 36R. Douc, C. Matias.

    Asymptotics of the maximum likelihood estimator for general hidden Markov models, in: Bernoulli, June 2001, vol. 7, no 3, pp. 381–420.
  • 37D. 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.
  • 38D. 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.
  • 39D. Frenkel, B. Smit.

    Understanding molecular simulation. From algorithms to applications, Computational Science Series, 2nd, Academic Press, San Diego, 2002, vol. 1.
  • 40P. Glasserman.

    Monte Carlo methods in financial engineering, Applications of Mathematics, Springer–Verlag, New York, 2004, vol. 53.
  • 41P. 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.
  • 42N. 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.
  • 43F. 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.
  • 44M. 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.
  • 45M. R. James, F. Le Gland.

    Consistent parameter estimation for partially observed diffusions with small noise, in: Applied Mathematics & Optimization, July/August 1995, vol. 32, no 1, pp. 47–72.
  • 46M. Joannides, F. Le Gland.

    Small noise asymptotics of the Bayesian estimator in nonidentifiable models, in: Statistical Inference for Stochastic Processes, 2002, vol. 5, no 1, pp. 95–130.
  • 47G. 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.
  • 48Y. A. Kutoyants.

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  • 49H. R. Künsch.

    Recursive Monte Carlo filters : Algorithms and theoretical analysis, in: The Annals of Statistics, October 2005, vol. 33, no 5, pp. 1983–2021.
  • 50P. 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.
  • 51L. Le Cam.

    Asymptotic methods in statistical decision theory, Springer Series in Statistics, Springer–Verlag, New York, 1986.
  • 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.
  • 54F. Le Gland, B. Wang.

    Asymptotic normality in partially observed diffusions with small noise : application to FDI, in: Workshop on Stochastic Theory and Control, University of Kansas 2001. In honor of Tyrone E. Duncan on the occasion of his 60th birthday, Berlin, B. Pasik–Duncan (editor), Lecture Notes in Control and Information Sciences, Springer–Verlag, 2002, no 280, pp. 267–282.
  • 55J. S. Liu.

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

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  • 59S. Thrun, W. Burgard, D. Fox.

    Probabilistic robotics, Intelligent Robotics and Autonomous Agents, The MIT Press, Cambridge, MA, 2005.
  • 60A. W. van der Vaart, J. A. Wellner.

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