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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
  • 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.

    http://dx.doi.org/10.1214/13-AIHP590
  • 4F. 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
  • 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.

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

    http://dx.doi.org/10.1007/s00245-011-9135-z
  • 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.

    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

  • 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 ]

    https://hal.archives-ouvertes.fr/hal-01394059
  • 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 ]

    https://hal.archives-ouvertes.fr/hal-01490572
  • 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 ]

    https://hal.archives-ouvertes.fr/hal-01625051
  • 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 ]

    https://hal.archives-ouvertes.fr/hal-01250058

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 ]

    https://hal.archives-ouvertes.fr/hal-01429975
  • 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.

    https://hal.inria.fr/hal-01590713
  • 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 ]

    https://hal.inria.fr/hal-01671149

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.

    https://hal.inria.fr/hal-01493840

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.

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

    http://dx.doi.org/10.1109/78.978374
  • 24M. Bocquel.

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

    http://purl.utwente.nl/publications/87496
  • 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.

    http://dx.doi.org/10.1109/JPROC.2007.893250
  • 26D. J. Daley, J. Gani.

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

    http://dx.doi.org/10.1017/CBO9780511608834
  • 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.

    http://dx.doi.org/10.1007/s10479-005-5724-z
  • 28P. 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
  • 29P. 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
  • 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.

    http://dx.doi.org/10.1007/BFb0103798
  • 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.

    http://dx.doi.org/10.1214/07-AOS514
  • 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.

    http://dx.doi.org/10.1109/MPRV.2003.1228524
  • 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.

    http://dx.doi.org/10.1007/978-1-4757-3437-9_19
  • 34D. Frenkel, B. Smit.

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

    http://www.sciencedirect.com/science/book/9780122673511
  • 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.

    http://dx.doi.org/10.1063/1.1378322
  • 36P. 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
  • 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.

    http://dx.doi.org/10.1287/opre.47.4.585
  • 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.

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

    http://dx.doi.org/10.1109/78.978396
  • 40J. Houssineau.

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

    https://jeremiehoussineau.wordpress.com/ph-d-thesis-all-versions/
  • 41P. Héas, C. Herzet.

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

    https://arxiv.org/abs/1610.02962
  • 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.

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

    http://www.jstor.org/stable/1390750
  • 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.

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

    http://dx.doi.org/10.1145/1225275.1225280
  • 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.

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

    http://dx.doi.org/10.1214/aoap/1075828050
  • 48J. S. Liu.

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

    http://dx.doi.org/10.1007/978-0-387-76371-2
  • 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 ]

    https://hal.archives-ouvertes.fr/hal-01661012
  • 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.

    http://dx.doi.org/10.1109/TAES.2003.1261119
  • 51B. Ristić, M. S. Arulampalam, N. J. Gordon.

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

    http://www.artechhouse.com/international/books/945.aspx
  • 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.

    http://dx.doi.org/10.1007/978-1-4757-4321-0
  • 53C. 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
  • 54L. D. Stone, R. L. Streit, T. L. Corwin, K. L. Bell.

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

    http://www.artechhouse.com/international/books/2090.aspx
  • 55S. Thrun, W. Burgard, D. Fox.

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

    http://mitpress.mit.edu/books/probabilistic-robotics
  • 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.

    http://dx.doi.org/10.1109/TSP.2006.881190
  • 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 ]

    https://hal.inria.fr/hal-01423198
  • 58A. W. van der Vaart, J. A. Wellner.

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

    http://dx.doi.org/10.1007/978-1-4757-2545-2


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