A backward-simulation based Rao-Blackwellized particle smoother for conditionally linear Gaussian models

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 16, Issue PART 1, 2012, Pages 506-511

  Särkkä, Simo ^a   Bunch, Pete ^b   Godsill, Simon J ^b  

^a AALTO UNIVERSITY   (Finland)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Estimation algorithms; Monte Carlo method; Nonlinear systems; Optimal estimation; Stochastic systems

Indexed keywords

GAUSSIAN DISTRIBUTION; NONLINEAR SYSTEMS; STOCHASTIC SYSTEMS;

BACKWARD SIMULATIONS; CONDITIONALLY LINEAR GAUSSIAN; ESTIMATION ALGORITHM; FORWARD-FILTERING; MONTE CARLO SMOOTHING; NON-GAUSSIAN STATE; OPTIMAL ESTIMATIONS; PARTICLE SMOOTHERS;

MONTE CARLO METHODS;

EID: 84867094916     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20120711-3-BE-2027.00115     Document Type: Conference Paper

References (18)

1. Y. Bar-Shalom, X.-R. Li, and T. Kirubarajan. Estimation with Applications to Tracking and Navigation. Wiley Interscience, 2001.
2. D. Barber. Expectation correction for smoothed inference in switching linear dynamical systems. Journal of Machine Learning Research, 7:2515-2540, 2006.
3. M. Briers, A. Doucet, and S. Maskell. Smoothing algorithms for state-space models. Ann Inst Stat Math, 62: 61-89, 2010.
4. O. Cappé, S. J. Godsill, and E. Moulines. An overview of existing methods and recent advances in sequential Monte Carlo. Proceedings of the IEEE, 95(5):899-924, 2007.
5. R. Chen and J. S. Liu. Mixture Kalman filters. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 62(3):493-508, 2000.
6. A. Doucet, S. J. Godsill, and C. Andrieu. On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10(3):197-208, 2000.
7. P. Fearnhead, D. Wyncoll, and J. Tawn. A sequential smoothing algorithm with linear computational cost. Biometrika, 97(2):447-464, 2010.
8. W. Fong, S. J. Godsill, A. Doucet, and M. West. Monte Carlo smoothing with application to audio signal enhancement. IEEE Transactions on Signal Processing, 50(2):438-449, 2002.
9. S. J. Godsill, A. Doucet, and M. West. Monte Carlo smoothing for nonlinear time series. Journal of the American Statistical Association, 99(465):156-168, 2004.
10. N. J. Gordon, D. J. Salmond, and A. F. M. Smith. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F, 140(2):107-113, 1993.
11. Y. C. Ho and R. C. K. Lee. A Bayesian approach to problems in stochastic estimation and control. IEEE Transactions on Automatic Control, 9:333-339, 1964.
12. M. Hürzeler and H. R. Kunsch. Monte Carlo approximations for general state-space models. Journal of Computational and Graphical Statistics, 7(2):175-193, 1998.
13. C-J. Kim. Dynamic linear models with Markov-switching. Journal of Econometrics, 60:1-22, 1994.
14. G. Kitagawa. The two-filter formula for smoothing and an implementation of the Gaussian-sum smoother. Annals of the Institute of Statistical Mathematics, 46:605-623, 1994.
15. G. Kitagawa. Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5:1-25, 1996.
16. R. C. K. Lee. Optimal Estimation, Identification and Control. M.I.T. Press, 1964.
17. F. Lindsten and T. B. Schön. Rao-Blackwellized particle smoothers for mixed linear/nonlinear state-space models. Technical Report LiTH-ISY-R-2018, Linköpings Universitet, 2011.
18. B. Ristic, S. Arulampalam, and N. Gordon. Beyond the Kalman Filter. Artech House, 2004.