EN FR
EN FR


previous Previous | up Home


Bibliography

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.

    http://jmlr.csail.mit.edu/papers/v11/biau10a.html
  • 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.

    http://dx.doi.org/10.1109/TIT.2010.2040857
  • 3F. Cérou, A. Guyader.

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

    http://dx.doi.org/10.1051/ps:2006014
  • 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.

    http://dx.doi.org/10.1080/07362990601139628
  • 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.

    http://dx.doi.org/10.1007/978-3-540-88961-8_24
  • 7F. 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.
  • 8F. 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.

    http://dx.doi.org/10.1007/PL00009861
  • 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.

    http://dx.doi.org/10.1007/11587392_11
  • 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.

    http://dx.doi.org/10.1007/978-1-4757-3437-9_12
Publications of the year

Articles in International Peer-Reviewed Journals

  • 11P. Ailliot, D. Allard, V. Monbet, P. Naveau.

    Stochastic weather generators: an overview of weather type models, in: Journal de la Société Française de Statistique, 2015, vol. 156, no 1, pp. 101-113.

    https://hal.archives-ouvertes.fr/hal-01167055
  • 12P. Ailliot, J. Bessac, V. Monbet, F. Pène.

    Non-homogeneous hidden Markov-switching models for wind time series, in: Journal of Statistical Planning and Inference, 2015, vol. 160, pp. 75-88. [ DOI : 10.1016/j.jspi.2014.12.005 ]

    https://hal.archives-ouvertes.fr/hal-00974716
  • 13J.-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, forthcoming. [ DOI : 10.1016/j.jbspin.2015.05.009 ]

    https://hal-univ-rennes1.archives-ouvertes.fr/hal-01243032
  • 14J. Bessac, P. Ailliot, V. Monbet.

    Gaussian linear state-space model for wind fields in the North-East Atlantic, in: Environmetrics, February 2015, vol. 26, no 1, pp. 29–38. [ DOI : 10.1002/env.2299 ]

    https://hal.inria.fr/hal-01100142
  • 15G. Biau, F. Cérou, A. Guyader.

    New insights into Approximate Bayesian Computation, in: Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques, February 2015, vol. 51, no 1, pp. 376-403. [ DOI : 10.1214/13-AIHP590 ]

    https://hal.archives-ouvertes.fr/hal-00721164
  • 16A. Guyader, N. Hengartner, N. Jégou, E. Matzner-Løber.

    Iterative Isotonic Regression, in: ESAIM: Probability and Statistics, 2015, vol. 19, pp. 1-23. [ DOI : 10.1051/ps/2014012 ]

    https://hal.archives-ouvertes.fr/hal-00832863
  • 17D. Jacquemart, J. Morio, F. Le Gland.

    Some ideas for bias and variance reduction in the splitting algorithm for diffusion processes, in: Journal of Computational Science, November 2015, vol. 11, pp. 58–68. [ DOI : 10.1016/j.jocs.2015.09.005 ]

    https://hal.inria.fr/hal-01253763
  • 18K. Léon, K. Pichavant-Rafini, H. Ollivier, V. Monbet, E. L'HER.

    Does Induction Time of Mild Hypothermia Influence the Survival Duration of Septic Rats?, in: Therapeutic Hypothermia and Temperature Management, June 2015, vol. 5, no 2, pp. 85-88. [ DOI : 10.1089/ther.2014.0024 ]

    https://hal.archives-ouvertes.fr/hal-01259883
  • 19V. Monbet.

    Editorial to the special issue on stochastic weather generators, in: Journal de la Société Française de Statistique, 2015, vol. 156, no 1, pp. 97-98, Numéro spécial : Génération aléatoire de conditions météorologiques.

    https://hal.archives-ouvertes.fr/hal-01240896
  • 20R. Pastel, J. Morio, F. Le Gland.

    Extreme density level set estimation for input–output functions via the adaptive splitting technique, in: Journal of Computational Science, January 2015, vol. 6, pp. 40–46. [ DOI : 10.1016/j.jocs.2014.11.001 ]

    https://hal.inria.fr/hal-01110391

International Conferences with Proceedings

  • 21P. Bui Quang, C. Musso, F. Le Gland.

    The Kalman Laplace filter: a new deterministic algorithm for nonlinear Bayesian filtering, in: Proceedings of the 18th International Conference on Information Fusion, Washington DC 2015, Washington DC, United States, July 2015, pp. 1657–1663.

    https://hal.inria.fr/hal-01183413

Conferences without Proceedings

  • 22P. Héas, C. Herzet.

    Inverse Reduced-Order Modeling, in: Reduced Basis, POD and PGD Model Reduction Techniques, Cachan, France, November 2015.

    https://hal.inria.fr/hal-01245051

Scientific Books (or Scientific Book chapters)

  • 23Y. Kenné, F. Le Gland, C. Musso, S. Paris, Y. Glemarec, E. Vasta.

    Simulation-based algorithms for the optimization of sensor deployment, in: Proceedings of the 3rd International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences, Metz 2015, H. A. L. Thi, T. P. Dinh, N. T. Nguyen (editors), Advances in Intelligent Systems and Computing, Springer, May 2015, vol. 360, pp. 261-272. [ DOI : 10.1007/978-3-319-18167-7_23 ]

    https://hal.inria.fr/hal-01183819
  • 24P. TANDEO, 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: proceedings of the 4th International Workshop on Climate Informatics, Springer, 2015, pp. 3 - 12. [ DOI : 10.1007/978-3-319-17220-0_1 ]

    https://hal.archives-ouvertes.fr/hal-01202496

Other Publications

  • 25P. Ailliot, B. Delyon, V. Monbet, M. Prevosto.

    Dependent time changed processes with applications to nonlinear ocean waves, 2015, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-01219717
  • 26J. 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, January 2016, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-01250353
  • 27P. Héas, A. Drémeau, C. Herzet.

    An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model, December 2015, 37 pages.

    https://hal.inria.fr/hal-01158551
  • 28P. Héas, C. Herzet.

    Reduced-Order Modeling Of Hidden Dynamics, December 2015, working paper or preprint.

    https://hal.inria.fr/hal-01246074
  • 29V. Monbet, P. Ailliot.

    Sparse vector Markov switching autoregressive models Application to multiple time series of air temperature, January 2016, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-01250058
References in notes
  • 30A. 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.

    http://dx.doi.org/10.1007/978-1-4757-3437-9
  • 31M. 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.

    http://dx.doi.org/10.1109/78.978374
  • 32O. 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.

    http://dx.doi.org/10.1109/JPROC.2007.893250
  • 33O. Cappé, É. Moulines, T. Rydén.

    Inference in hidden Markov models, Springer Series in Statistics, Springer–Verlag, New York, 2005.

    http://dx.doi.org/10.1007/0-387-28982-8
  • 34P.–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.
  • 35P. Del Moral.

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

    http://www.crcpress.com/product/isbn/9781466504059
  • 36P. Del Moral.

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

    http://dx.doi.org/10.1007/978-1-4684-9393-1
  • 37P. 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.
  • 38P. 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.

    http://dx.doi.org/10.1007/BFb0103798
  • 39R. 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.
  • 40R. 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.

    http://dx.doi.org/10.1214/07-AOS514
  • 41R. Douc, É. Moulines, D. S. Stoffer.

    Nonlinear time series: Theory, methods and applications with R examples, Texts in Statistical Science, Chapman & Hall / CRC Press, Boca Raton, 2014.

    http://www.crcpress.com/product/isbn/9781466502253
  • 42E. B. Dynkin, R. J. Vanderbei.

    Stochastic waves, in: Transactions of the American Mathematical Society, February 1983, vol. 275, no 2, pp. 771–779.

    http://dx.doi.org/10.2307/1999052
  • 43D. 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.
  • 44D. 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.
  • 45D. Frenkel, B. Smit.

    Understanding molecular simulation. From algorithms to applications, Computational Science Series, 2nd, Academic Press, San Diego, 2002, vol. 1.
  • 46P. Glasserman.

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

    http://dx.doi.org/10.1007/978-0-387-21617-1
  • 47P. 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.

    http://dx.doi.org/10.1287/opre.47.4.585
  • 48E. Gobet, S. Menozzi.

    Stopped diffusion processes: Boundary corrections and overshoot, in: Stochastic Processes and their Applications, February 2010, vol. 120, no 2, pp. 130–162.

    http://dx.doi.org/10.1016/j.spa.2009.09.014
  • 49N. 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.

    http://dx.doi.org/10.1049/ip-f-2.1993.0015
  • 50F. 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.

    http://dx.doi.org/10.1109/78.978396
  • 51M. 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.

    http://dx.doi.org/10.1023/A:1008078328650
  • 52M. 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.

    http://dx.doi.org/10.1007/BF01189903
  • 53M. 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.

    http://dx.doi.org/10.1023/A:1013737907166
  • 54G. 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.

    http://www.jstor.org/stable/1390750
  • 55Y. A. Kutoyants.

    Identification of dynamical systems with small noise, Mathematics and its Applications, Kluwer Academic Publisher, Dordrecht, 1994, vol. 300.
  • 56H. R. Künsch.

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

    http://www.jstor.org/stable/3448632
  • 57P. 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.

    http://dx.doi.org/10.1145/1225275.1225280
  • 58L. Le Cam.

    Asymptotic methods in statistical decision theory, Springer Series in Statistics, Springer–Verlag, New York, 1986.
  • 59F. 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.

    http://dx.doi.org/10.1016/S0304-4149(03)00041-3
  • 60F. 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.

    http://dx.doi.org/10.1214/aoap/1075828050
  • 61F. 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.
  • 62J. S. Liu.

    Monte Carlo strategies in scientific computing, Springer Series in Statistics, Springer–Verlag, New York, 2001.
  • 63B. Ristić, M. S. Arulampalam, N. J. Gordon.

    Beyond the Kalman Filter : Particle Filters for Tracking Applications, Artech House, Norwood, MA, 2004.
  • 64R. 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.
  • 65C. J. Stone.

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

    http://www.jstor.org/stable/2958783
  • 66S. Thrun, W. Burgard, D. Fox.

    Probabilistic robotics, Intelligent Robotics and Autonomous Agents, The MIT Press, Cambridge, MA, 2005.
  • 67D. Villemonais.

    General approximation method for the distribution of Markov processes conditioned not to be killed, in: ESAIM: Probability and Statistics, 2014, vol. 18, pp. 441–467.

    http://dx.doi.org/10.1051/ps/2013045
  • 68A. W. van der Vaart, J. A. Wellner.

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


previous Previous | up Home