482
Litt´ erature
51. P. Del Moral and J. Jacod. The Monte-Carlo method for filtering with
discrete-time observations : Central limit theorems. In Numerical Methods and
stochastics (Toronto, ON, 1999), volume 34 of Fields Inst. Commun., pages
29–53. American Mathematical Society, Providence, RI (2002).
52. P. Del Moral, L. Miclo. Genealogies and Increasing Propagations of Chaos
for Feynman-Kac and Genetic Models. Annals of Applied Probability, vol. 11,
No. 4, pp. 1166-1198 (2001).
53. A.P. Dempster, N.M. Laird et D.B. Rubin. Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society.
Series B (Methodological), vol. 39, no 1, p. 1–38 (1977).
54. W. Doeblin. Sur l’´ equation de Kolmogoroff, Pli cachet´ e ? l’Acad´ emie des
Sciences, ´ edit´ e par B. Bru et M. Yor, CRAS, Paris, 331 (2000).
55. A. Doucet, N. de Freitas, and N. Gordon, editors. Sequential Monte
Carlo Methods in Pratice. Statistics for engineering and Information Science.
Springer, New York (2001).
56. P. Drineas, R. Kannan, M.W. Mahoney. Fast Monte Carlo algorithms for
matrices I : Approximating matrix multiplication. SIAM J. Comput. Vol. 36,
No. 1, pp. 132-157 (2006).
57. S. Drulhe, G. Ferrari-Trecate, H. de Jong, and A. Viari. Reconstruction
of switching thresholds in piecewise-affne models of genetic regulatory networks.
In J. Hespanha and A. Tiwari, editors, Hybrid Systems : Computation and
Control, volume 3927 of Lecture Notes in Computer Science, pages 184 ?199.
Springer Verlag, Berlin (2006).
58. M. Duflo. Random Iterative Models. Springer-Verlag Berlin and New York
(1997).
59. F. J. Dyson. A Brownian-motion model for the eigenvalues of a random matrix.
J. Math. Phys., vol. 3, pp. 1191–1198 (1962).
60. M. Field, M. Golubitsky, La sym´ etrie du chaos, `
a la recherche des liens entre
math´ ematiques et nature, Inter- ´
Editions, Paris (1993).
61. S. N. Ethier and T. G. Kurtz. Markov Processes : Characterization and
Convergence, Wiley Series Probability & Statistics (1986).
62. G. Evensen. Sequential data assimilation with a nonlinear quasi-geostrophic
model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research (Oceans), vol. 99, no. C5, pp. 10143–10162 (1994).
63. G. Evensen. Ensemble Kalman filter : Theoretical formulation and practical
implementations. Ocean Dynamics, vol. 53, no. 4, pp. 343–367 (2003).
64. G. Evensen. Data Assimilation : The Ensemble Kalman Filter. SpringerVerlag, Berlin (2006).
65. G. Fishman. Monte Carlo. Concepts, Algorithms and Applications. SpringerVerlag (1996).
66. T. Fitzgibbons, E. Nebot. Bearing Only SLAM using Colour-based feature
Tracking. Proceedings 2002 Australian Conference on Robotics and Automation.
Auckland.
67. A. Friedman. Stochastic differential equations and applications. Vol. 1. Academic Press New York. Series : Probability and Mathematical Statistics, Vol. 28
(1975).
Litt´ erature
51. P. Del Moral and J. Jacod. The Monte-Carlo method for filtering with
discrete-time observations : Central limit theorems. In Numerical Methods and
stochastics (Toronto, ON, 1999), volume 34 of Fields Inst. Commun., pages
29–53. American Mathematical Society, Providence, RI (2002).
52. P. Del Moral, L. Miclo. Genealogies and Increasing Propagations of Chaos
for Feynman-Kac and Genetic Models. Annals of Applied Probability, vol. 11,
No. 4, pp. 1166-1198 (2001).
53. A.P. Dempster, N.M. Laird et D.B. Rubin. Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society.
Series B (Methodological), vol. 39, no 1, p. 1–38 (1977).
54. W. Doeblin. Sur l’´ equation de Kolmogoroff, Pli cachet´ e ? l’Acad´ emie des
Sciences, ´ edit´ e par B. Bru et M. Yor, CRAS, Paris, 331 (2000).
55. A. Doucet, N. de Freitas, and N. Gordon, editors. Sequential Monte
Carlo Methods in Pratice. Statistics for engineering and Information Science.
Springer, New York (2001).
56. P. Drineas, R. Kannan, M.W. Mahoney. Fast Monte Carlo algorithms for
matrices I : Approximating matrix multiplication. SIAM J. Comput. Vol. 36,
No. 1, pp. 132-157 (2006).
57. S. Drulhe, G. Ferrari-Trecate, H. de Jong, and A. Viari. Reconstruction
of switching thresholds in piecewise-affne models of genetic regulatory networks.
In J. Hespanha and A. Tiwari, editors, Hybrid Systems : Computation and
Control, volume 3927 of Lecture Notes in Computer Science, pages 184 ?199.
Springer Verlag, Berlin (2006).
58. M. Duflo. Random Iterative Models. Springer-Verlag Berlin and New York
(1997).
59. F. J. Dyson. A Brownian-motion model for the eigenvalues of a random matrix.
J. Math. Phys., vol. 3, pp. 1191–1198 (1962).
60. M. Field, M. Golubitsky, La sym´ etrie du chaos, `
a la recherche des liens entre
math´ ematiques et nature, Inter- ´
Editions, Paris (1993).
61. S. N. Ethier and T. G. Kurtz. Markov Processes : Characterization and
Convergence, Wiley Series Probability & Statistics (1986).
62. G. Evensen. Sequential data assimilation with a nonlinear quasi-geostrophic
model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research (Oceans), vol. 99, no. C5, pp. 10143–10162 (1994).
63. G. Evensen. Ensemble Kalman filter : Theoretical formulation and practical
implementations. Ocean Dynamics, vol. 53, no. 4, pp. 343–367 (2003).
64. G. Evensen. Data Assimilation : The Ensemble Kalman Filter. SpringerVerlag, Berlin (2006).
65. G. Fishman. Monte Carlo. Concepts, Algorithms and Applications. SpringerVerlag (1996).
66. T. Fitzgibbons, E. Nebot. Bearing Only SLAM using Colour-based feature
Tracking. Proceedings 2002 Australian Conference on Robotics and Automation.
Auckland.
67. A. Friedman. Stochastic differential equations and applications. Vol. 1. Academic Press New York. Series : Probability and Mathematical Statistics, Vol. 28
(1975).