Efficient particle-based online smoothing in general hidden Markov models

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2014, Pages 8003-8007

  Westerborn, Johan ^a   Olsson, Jimmy ^a  

^a ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

Hidden Markov models; Monte Carlo methods; particle filters; smoothing methods; state estimation

Indexed keywords

ESTIMATION; HIDDEN MARKOV MODELS; MONTE CARLO METHODS; SIGNAL PROCESSING; STATE ESTIMATION;

COMPUTATIONAL COSTS; COMPUTATIONALLY EFFICIENT; DEGENERACY PROBLEMS; ON-LINE ESTIMATION; ONLINE EXPECTATION MAXIMIZATIONS; PARAMETER INFERENCE; PARTICLE FILTER; SMOOTHING METHODS;

ALGORITHMS;

EID: 84905270424     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2014.6855159     Document Type: Conference Paper

References (12)

1. Oliver Cappé, Eric Moulines, and Tobias Rydén, Inference in Hidden Markov Models, Springer, New York, 2005.
2. Pierre Del Moral, Arnaud Doucet, and Sumeetpal S. Singh, "Forward smoothing using sequential Monte Carlo, " Tech. Rep., Cambridge University, 2009.
3. Oliver Cappé, "Online sequential Monte Carlo EM algorithm, " in Statistical Signal Processing, 2009. SSP '09. IEEE/SP 15th Workshop on, 2009, pp. 37-40.
4. Oliver Cappé, "Online expectation-maximisation, " ArXiv eprints, Nov. 2010.
5. Christophe Andrieu and Arnaud Doucet, "Online expectationmaximization type algorithms for parameter estimation in general state space models, " in Proc. IEEE Conf. ICASSP, 2003, pp. 69-72.
6. N. J. Gordon, D. J. Salmond, and A. F. M. Smith, "Novel approach to nonlinear/non-Gaussian Bayesian state estimation, " IEE Proceedings F. Radar &Signal Processing, vol. 40, pp. 107-113, 1993.
7. Jimmy Olsson, Olivier Cappé, Randal Douc, and Eric Moulines, "Sequential Monte Carlo smoothing with application to parameter estimation in nonlinear state space models, " Bernoulli, vol. 14, pp. 155-179, 2008.
8. Christian P. Robert and George Casella, Monte Carlo statistical methods. 2nd ed., New York, NY: Springer, 2nd ed. edition, 2004.
9. Markus Hurzeler and Hans R. Kunsch, "Monte Carlo approximations for general state-space models, " Journal of Computational and Graphical Statistics, vol. 7, no. 2, pp. 175-193, 1998.
10. Arnaud Doucet, Simon Godsill, and Christophe Andrieu, "On sequential Monte Carlo sampling methods for Bayesian filtering, " Statistics and Computing, vol. 10, no. 3, pp. 197-208, 2000.
11. Randal Douc, Aurélien Garivier, Eric Moulines, and Jimmy Olsson, "Sequential Monte Carlo smoothing for general state space hidden Markov models, " The Annals of Applied Probability, vol. 21, pp. 2109-2145, 2011.
12. Simon J Godsill, Arnaud Doucet, and Mike West, "Monte Carlo smoothing for nonlinear time series, " Journal of the American Statistical Association, vol. 99, no. 465, pp. 156-168, 2004.