Free Access
Volume 16, 2012
Page(s) 151 - 164
Published online 03 July 2012
  1. R. Atar and O. Zeitouni, Exponential stability for nonlinear filtering. Ann. Inst. Henri Poincaré 33 (1997) 697–725. [CrossRef] [Google Scholar]
  2. O. Cappé, E. Moulines and T. Ryden, Inference in Hidden Markov Models, in Springer Series in Statistics. Springer-Verlag New York, Inc., Secaucus, NJ, USA (2005). [Google Scholar]
  3. M. Chaleyat-Maurel and V. Genon-Catalot, Computable infinite-dimensional filters with applications to discretized diffusion processes. Stoc. Proc. Appl. 116 (2006) 1447–1467. [CrossRef] [Google Scholar]
  4. N. Chopin, Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference. Ann. Statist. 32 (2004) 2385–2411. [CrossRef] [MathSciNet] [Google Scholar]
  5. E.B. Davies, Heat kernels and spectral theory, in Cambridge Tracts in Mathematics. Cambridge University Press, Cambridge 92 (1989). [Google Scholar]
  6. P. Del Moral, Feynman-Kac formulae, Genealogical and interacting particle systems with applications. Probab. Appl. Springer-Verlag, New York (2004). [Google Scholar]
  7. P. Del Moral and A. Guionnet, On the stability of interacting processes with applications to filtering and genetic algorithms. Ann. Inst. Henri Poincaré 37 (2001) 155–194. [Google Scholar]
  8. P. Del Moral and J. Jacod, Interacting particle filtering with discrete observations, in Sequential Monte Carlo methods in practice, Springer, New York. Stat. Eng. Inf. Sci. (2001) 43–75. [Google Scholar]
  9. P. Del Moral and J. Jacod, Interacting particle filtering with discrete-time observations : asymptotic behaviour in the Gaussian case, in Stochastics in finite and infinite dimensions, Birkhäuser Boston, Boston, MA. Trends Math. (2001) 101–122. [Google Scholar]
  10. R. Douc, A. Guillin and J. Najim, Moderate deviations for particle filtering. Ann. Appl. Probab. 15 (2005) 587–614. [CrossRef] [MathSciNet] [Google Scholar]
  11. R. Douc, G. Fort, E. Moulines and P. Priouret, Forgetting of the initial distribution for hidden Markov models. Stoc. Proc. Appl. 119 (2009) 1235–1256. [CrossRef] [Google Scholar]
  12. A. Doucet, N. de Freitas and N. Gordon, Sequential Monte Carlo methods in practice, Stat. Eng. Inform. Sci. Springer-Verlag, New York (2001). [Google Scholar]
  13. H.R. Künsch, State space and hidden Markov models, in Complex Stochastic Systems. Eindhoven (1999); Chapman & Hall/CRC, Boca Raton, FL. Monogr. Statist. Appl. Probab. 87 (2001) 109–173. [Google Scholar]
  14. H.R. Künsch, Recursive Monte Carlo filters : algorithms and theoretical analysis. Ann. Statist. 33 (2005) 1983–2021. [CrossRef] [MathSciNet] [Google Scholar]
  15. N. Oudjane and S. Rubenthaler, Stability and uniform particle approximation of nonlinear filters in case of non ergodic signals. Stoch. Anal. Appl. 23 (2005) 421–448. [CrossRef] [MathSciNet] [Google Scholar]
  16. C.P. Robert and G. Casella, Monte Carlo statistical methods, 2nd edition, in Springer Texts in Statistics. Springer-Verlag, New York (2004). [Google Scholar]
  17. R. Van Handel, Uniform time average consistency of Monte Carlo particle filters. Stoc. Proc. Appl. 119 (2009) 3835–3861. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.