Adaptive methods for sequential importance sampling with application to state space models

European Signal Processing Conference

Volumn , Issue , 2008, Pages

  Cornebise, Julien ^a   Moulines, Éric ^a   Olsson, Jimmy ^b  

^a TELECOM PARISTECH   (France)

^b LUND UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE METHODS; AUXILIARY PARTICLE FILTER; COEFFICIENT OF VARIATION; EMPIRICAL ESTIMATE; KULLBACK LEIBLER DIVERGENCE; LINEAR COMPLEXITY; NUMERICAL EXAMPLE; PARTICLE FILTER; PROPOSAL DISTRIBUTION; SEQUENTIAL IMPORTANCE SAMPLING; SEQUENTIAL MONTE CARLO; STATE SPACE MODEL;

DISTRIBUTED COMPUTER SYSTEMS; ESTIMATION; NONLINEAR FILTERING; SIGNAL PROCESSING; STATE SPACE METHODS;

TARGET TRACKING;

EID: 84863761791     PISSN: 22195491     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. T. Bollerslev, R. F. Engle, and D. B. Nelson, "ARCH models", in The Handbook of Econometrics (R. F. Engle and D. MacFadden eds.), vol. 4, pp. 2959-3038, 1994.
2. B. Chan, A. Doucet, A., and V. B. Tadic, "Optimization of Particle Filters Using Simultaneous Perturbation Stochastic Approximation", Proc. IEEE ICASSP'03, 2003.
3. J. Cornebise, É. Moulines, and J. Olsson, "Adaptive Methods for Sequential Importance Sampling with Application to State Space Models", Technical report Lund University, 2008. URL http://arxiv.org/abs/0803.0054.
4. R. Douc and É. Moulines, "Limit theorems for weighted samples with applications to sequential Monte Carlo methods", to appear in Ann. Stat.
5. R. Douc, É. Moulines, and J. Olsson, "On the auxiliary particle filter", Technical report ENST, 2007. URL http://arXiv.org/abs/ 0709.3448.
6. A. Doucet, S. Godsill, and C. Andrieu, "On sequential Monte-Carlo Sampling Methods for Bayesian Filtering", Stat. Comput, vol. 10, 197-208, 2000.
7. D. Fox, "Adapting the sample size in particle filters through KLD-sampling", International Journal of Robotics Research, vol. 22, pp. 985-1004, 2003.
8. N. J. Gordon, D. J. Salmond, and A. F. M. Smith, "Novel approach to non-linear/non-Gaussian Bayesian state estimation", IEEE Proc. Comm. Radar Signal Proc., vol. 140, pp. 107-113, 1993.
9. M. Hürzeler and H. R. Künsch, "Monte Carlo approximations for general state space models", J. Comput. Graph. Statist., vol. 7, pp. 175-193, 1998.
10. A. Kong, J. S. Liu, and W. Wong, "Sequential imputation and Bayesian missing data problems", J. Am. Statist. Assoc., vol. 89, pp. 278-288, 1994.
11. H. R. Künsch, "Recursive Monte Carlo filters: algorithms and theoretical analysis", Ann. Stat., vol. 33, pp. 1983-2021, 2005.
12. F. Legland and N. Oudjane, "A sequential particle algorithm that keeps the particle system alive", Rapport de Recherche 5826, INRIA, 2006. URL ftp://ftp.inria.fr/INRIA/publication/publi-pdf/RR/RR-5826.pdf.
13. R. A. Levine, and G. Casella, "Implementations of the Monte Carlo EM algorithm", J. Comput. Graph. Statist., vol. 10, pp. 422-439, 2001.
14. M. K. Pitt, and N. Shephard, "Filtering via simulation: Auxiliary particle filters", J. Am. Statist. Assoc., vol. 87, pp. 493-499, 1999.
15. R. Y. Rubinstein, and D. P. Kroese, The Cross-Entropy Method, Springer, 2004.