On-line inference for hidden Markov models via particle filters

Journal of the Royal Statistical Society. Series B: Statistical Methodology

Volumn 65, Issue 4, 2003, Pages 887-899

  Fearnhead, Paul ^a   Clifford, Peter ^b  

^a LANCASTER UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Changepoints; Ion channel; Kalman filter; Markov chain Monte Carlo methods; Particle filters; Smoothing; Well log data

Indexed keywords

EID: 0242550819     PISSN: 13697412     EISSN: None     Source Type: Journal    
DOI: 10.1111/1467-9868.00421     Document Type: Article

References (25)

1. Akashi, H. and Kumamoto, H. (1977) Random sampling approach to state estimation in switching environments. Automatica, 13, 429-434.
2. Ball, F. G. and Rice, J. A. (1992) Stochastic models for ion channels: introduction and bibliography. Math. Biosci., 112, 189-206.
3. Blum, M., Floyd, R. W., Pratt, V., Rivest, R. L. and Tarjan, R. E. (1973) Time bounds for selection. J. Comput. Syst. Sci., 7, 448-461.
4. Carpenter, J., Clifford, P. and Fearnhead, P. (1999) An improved particle filter for non-linear problems. IEE Proc. Radar Sonar Navign, 146, 2-7.
5. Chen, R. and Liu, J. S. (2000) Mixture Kalman filters. J. R. Statist. Soc. B, 62, 493-508.
6. Cormen, T. H., Leiserson, C. E. and Rivest, R. L. (1996) Introduction to Algorithms. Cambridge: MIT Press.
7. Doucet, A., de Freitas, J. F. G. and Gordon, N. J. (eds) (2001) Sequential Monte Carlo Methods in Practice. New York: Springer.
8. Durbin, R., Eddy, S., Krogh, A. and Mitchison, G. (1998) Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge: Cambridge University Press.
9. Fearnhead, P. (1998) Sequential Monte Carlo methods in filter theory. PhD Thesis. Oxford University, Oxford. (Available from http://www.maths.lancs.ac.uk/̃fearnhea/.)
10. Fearnhead, P. (2002a) MCMC, sufficient statistics and particle filters. J. Comput. Graph. Statist., 11, 848-862.
11. Fearnhead, P. (2002b) Particle filters for mixture models with an unknown number of components. Submitted to Comput. Statist. (Available from http://www.maths.lancs.ac.uk/̃fearnhea.)
12. Gilks, W. R. and Berzuini, C. (2001) Following a moving target - Monte Carlo inference for dynamic Bayesian models. J. R. Statist. Soc. B, 63, 127-146.
13. Gordon, N., Salmond, D. and Smith, A. F. M. (1993) Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc. F, 140, 107-113.
14. Green, P. (1995) Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82, 711-732.
15. Kitagawa, G. (1996) Monte Carlo filter and smoother for non-gaussian nonlinear state space models. J. Comput. Graph. Statist., 5, 1-25.
16. Liu, J. S. and Chen, R. (1995) Blind deconvolution via sequential imputations. J. Am. Statist. Ass., 90, 567-576.
17. Liu, J. S. and Chen, R. (1998) Sequential Monte Carlo methods for dynamic systems. J. Am. Statist. Ass., 93, 1032-1044.
18. Liu, J. S., Chen, R. and Logvinenko, T. (2001) A theoretical framework for sequential importance sampling with resampling. In Sequential Monte Carlo Methods in Practice (eds A. Doucet, J. F. G. de Freitas and N. J. Gordon), pp. 225-246. New York: Springer.
19. Liu, J. S., Chen, R. and Wong, W. H. (1996) Rejection control and importance sampling. Technical Report. Department of Statistics, Stanford University, Stanford.
20. MacEachern, S. N., Clyde, M. A. and Liu, J. S. (1999) Sequential importance sampling for nonparametric Bayes models: the next generation. Can. J. Statist., 27, 251-267.
21. Ó Ruanaidh, J. J. K. and Fitzgerald, W. J. (1996) Numerical Bayesian Methods applied to Signal Processing. New York: Springer.
22. Pitt, M. K. and Shephard, N. (1999) Filtering via simulation: auxiliary particle filters. J. Am. Statist. Ass., 94, 590-599.
23. Rabiner, L. R. and Juang, B. H. (1986) An introduction to hidden Markov models. IEEE Acoust. Spch Signal Process. Mag., 3, 4-16.
24. Tugnait, J. K. (1982) Detection and estimation for abruptly changing systems. Automatica, 18, 607-615.
25. West, M. (1992) Modelling with mixtures. In Bayesian Statistics 4 (eds J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith). Oxford: Clarendon.