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References
R. E. Kalman. A new approach to linear filtering and prediction problems. Trans. ASME Ser. D. J. Basic Engrg. vol. 82, pp. 35–45 (1960).
R. E. Kalman, R. S. Bucy. New results in linear filtering and prediction theory. Trans. ASME Ser. D. J. Basic Engrg.vol. 83, pp. 95–108 (1961).
P. Del Moral. Feynman-Kac formulae. Genealogical and interacting particle systems. Springer-Verlag: New York (2004).
P. Del Moral. Mean field simulation for Monte Carlo integration. Chapman & Hall/CRC Press. Monographs on Statistics and Applied Probability (2013).
P. Del Moral, J. Jacod. and P. Protter. The Monte-Carlo Method for filtering with discrete-time observations, Probability Theory and Related Fields, vol. 120, pp. 346–368, (2001).
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).
J.-P. Vila, V. Rossi. Nonlinear filtering in discrete time: A particle convolution approach, Biostatistic group of Montpellier, Technical Report 04–03, (available at http://vrossi.free.fr/recherche.html), (2004)
Th. Dean, S.S. Singh, A. Jasra, G.W. Peters. Parameter estimation for hidden Markov models with intractable likelihoods. ArXiv, preprint, arXiv:1103.5399v1 (2011).
P. Del Moral, A. Doucet, & A. Jasra. An Adaptive Sequential Monte Carlo Method for Approximate Bayesian Computation Statistics and Computing, vol. 22, no. 5, pp. 1009–1020 (2012).
A. Jasra, and S.S. Singh, J.S. Martin, and E. McCoy, E. Filtering via approximate Bayesian computation. Statistics and Computing pp. 1573–1375 (2010).
F. Le Gland, V. Monbet, and V. D. Tran. Large sample asymptotics for the ensemble Kalman filter. In Handbook on Nonlinear Filtering, D. Crisan and B. Rozovskii, Eds. Oxford University Press, Oxford, pp. 598–631 (2011).
V. Tran, V. Monbet and F. Le Gland. Filtre de Kalman d’ensemble et filtres particulaires pour le modèle de Lorenz. Actes de la Manifestation des Jeunes Chercheurs STIC (MajecSTIC’06), Lorient, November 22–24 (2006).
V. D. Tran. Assimilation de données: les propriétés asymptotiques du filtre de Kalman d’ensemble. Université de Bretagne Sud, June 29 (2009).
C. Baehr. Probabilistic models for atmospheric turbulence to filter measurements with particle approximations, Ph.D. thesis, Paul Sabatier University, Toulouse, Speciality: Applied Mathematics, Option Probability (2008).
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).
G. Evensen. Ensemble Kalman filter: Theoretical formulation and practical implementations. Ocean Dynamics, vol. 53, no. 4, pp. 343–367 (2003).
G. Evensen. Data Assimilation: The Ensemble Kalman Filter. Springer-Verlag, Berlin (2006).
J. L. Anderson, S. L. Anderson. A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Monthly Weather Review, vol. 127, no. 12, pp. 2741–2758 (1999).
G. Burgers, P. Jan van Leeuwen, and G. Evensen. Analysis scheme in the ensemble Kalman filter. Monthly Weather Review, vol. 126, no. 6, pp. 1719–1724 (1998).
A. W. Heemink, M.Verlaan, A. J. Segers. Variance reduced ensemble Kalman filtering. Monthly Weather Review, vol. 129, no. 7, pp. 1718–1728 (2001).
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Del Moral, P., Vergé, C. (2014). Traitement du signal. In: Modèles et méthodes stochastiques. Mathématiques et Applications, vol 75. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54616-7_12
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