Auxiliary particle filtering: Recent developments

Bayesian Time Series Models

Volumn 9780521196765, Issue , 2011, Pages 52-81

  Whiteley, Nick ^a   Johansen, Adam M ^b  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b UNIVERSITY OF WARWICK   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

MARKOV PROCESSES; STATE SPACE METHODS; VIDEO SIGNAL PROCESSING;

AUXILIARY PARTICLE FILTERING; CONTINUOUS-TIME; ESTIMATION PROBLEM; INTRINSIC PARAMETERS; PHYSICAL OBJECTS; POSTERIOR DISTRIBUTIONS; SCIENTIFIC DISCIPLINE; STATE - SPACE MODELS;

SIGNAL PROCESSING;

EID: 84906830447     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1017/CBO9780511984679.004     Document Type: Chapter

References (55)

1. C. Andrieu, M. Davy and A. Doucet. Efficient particle filtering for jump Markov systems. Application to time-varying autoregressions. IEEE Transactions on Signal Processing, 51(7):1762-1770, 2003.
2. A. Bain and D. Crisan. Fundamentals of Stochastic Filtering. Stochastic Modelling and Applied Probability. Springer Verlag, 2009.
3. M. Briers, A Doucet and S. S. Singh. Sequential auxiliary particle belief propagation. In Proceedings of International Conference on Information Fusion, 2005.
4. O. Cappé, E. Moulines and T. Ryden. Inference in Hidden Markov Models. Springer Verlag,2005.
5. J. Carpenter, P. Clifford and P. Fearnhead. An improved particle filter for non-linear problems. IEEE Proceedings on Radar, Sonar and Navigation, 146(1):2-7, 1999.
6. C. M. Carvalho and H. F. Lopes. Simulation-based sequential analysis of Markov switching stochastic volatility models. Computational Statistics and Data Analysis, 51:4526-4542, 2007.
7. S. Centanni and M. Minozzo. Estimation and filtering by reversible jump MCMC for a doubly stochastic Poisson model for ultra-high-frequency financial data. Statistical Modelling, 6(2):97-118, 2006.
8. N. Chopin. Central limit theorem for sequential Monte Carlo methods and its applications to Bayesian inference. Annals ofStatistics, 32(6):2385-2411, 2004.
9. D. E. Clark and J. Bell. Convergence results for the particle PHD filter. IEEE Transactions on Signal Processing, 54(7):2652-2661, July 2006.
10. J. Cornebise, E. Moulines and J. Olsson. Adaptive methods for sequential importance sampling with application to state space models. Statistics and Computing, 18:461-480, 2008.
11. D. J. Daley and D. Vere-Jones. An Introduction to the Theory of Point Processes, volume I: Elementary Theory and Methods of Probability and Its Applications. Springer, second edition, 2003.
12. A. Dassios and J. Jang. Kalman-Bucy filtering for linear system driven by the Cox process with shot noise intensity and its application to the pricing of reinsurance contracts. Journal of Applied Probability, 42(1):93-107, 2005.
13. P. Del Moral. Feynman-Kac Formulae: Genealogical and Interacting Particle Systems with applications. Probability and Its Applications. Springer Verlag, New York, 2004.
14. P. Del Moral, A. Doucet and A. Jasra. Sequential Monte Carlo methods for Bayesian Computation. In Bayesian Statistics 8. Oxford University Press, 2006.
15. P. Del Moral, A. Doucet and A. Jasra. Sequential Monte Carlo samplers. Journal of the Royal Statistical Society Series B, 63(3):411-436,2006.
16. P. Del Moral, A. Doucet and A. Jasra. On adaptive resampling procedures for sequential Monte Carlo methods. Technical Report HAL-INRIA RR-6700, INRIA, 2008.
17. R. Douc, O. Cappe and E. Moulines.Comparison of resampling schemes for particle filters. In Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, volume I, pages 64-69, 2005.
18. R. Douc and E. Moulines. Limit theorems for weighted samples with applications to sequential Monte Carlo methods. Annals ofStatistics, 36(5):2344-2376, 2008.
19. R. Douc, E. Moulines and J. Olsson. Optimality of the auxiliary particle filter. Probability and Mathematical Statistics, 29(1):1-28, 2009.
20. A. Doucet, N. de Freitas and N. Gordon, editors. Sequential Monte Carlo Methods in Practice. Statistics for Engineering and Information Science. Springer Verlag, 2001.
21. A. Doucet, N. Gordon and V. Krishnamurthy. Particle filters for state estimation of jump Markov linear systems. IEEE Transactions on Signal Processing, 49(3):613-624, 2001.
22. A. Doucet and A. M. Johansen. A tutorial on particle filtering and smoothing: Fifteen years later. In D. Crisan and B. Rozovsky, editors, The Oxford Handbook of Nonlinear Filtering. Oxford University Press, 2011. To appear.
23. P. Fearnhead. Sequential Monte Carlo methods in filter theory. PhD thesis, University of Oxford, 1998.
24. P. Fearnhead, O. Papaspiliopoulos and G. O. Roberts. Particle filters for partially observed diffusions. Journal of the Royal Statistical Society B, 70:755-777, 2008.
25. J. Geweke. Bayesian inference in econometric models using Monte Carlo integration. Econometrica, 57(6):1317-1339, 1989.
26. W. R. Gilks and Carlo Berzuini. RESAMPLE-MOVE filtering with Cross-Model jumps. In Doucet etal. [20], pages 117-138.
27. S. Godsill and T. Clapp. Improvement strategies for Monte Carlo particle filters. In Doucet et al. [20], pages 139-158.
28. S. J. Godsill, J. Vermaak, K-F. Ng J-F. Li. Models and algorithms for tracking of manoeuvring objects using variable rate particle filters. Proc. IEEE, April 2007.
29. A. G. Gray and A. W. Moore. N-body problems in statistical learning. In Advances in Neural Information Processing Systems 13, pages 521-527. MIT Press, 2000.
30. K. Heine. Unified framework for sampling/importance resampling algorithms. In Proceedings of Fusion 2005, July 25-9, 2005, Philadelphia. 2005.
31. A. M. Johansen. SMCTC: Sequential Monte Carlo in C++. Journal of Statistical Software, 30(6):1-41, 2009.
32. A. M. Johansen and A. Doucet. Auxiliary variable sequential Monte Carlo methods. Research Report 07:09, University of Bristol, Department of Mathematics - Statistics Group, niversity Walk, Bristol, BS8 1TW, UK, 2007.
33. A. M. Johansen and A. Doucet. A note on the auxiliary particle filter. Statistics and Probability Letters, 78(12):1498-1504, 2008.
34. A. M. Johansen, S. Singh, A. Doucet and B. Vo. Convergence of the SMC implementation of the PHD filter. Methodology and Computing in Applied Probability, 8(2):265-291, 2006.
35. N. Kantas. Sequential Decision Making in General State Space Models. PhD thesis, University of Cambridge, 2009.
36. R. Karlsson and N. Bergman. Auxiliary particle filters for tracking a maneuvering target. In Proceedings of the 39th IEEE Conference on Decision and Control, pages 3891-3895, 2000.
37. G. Kitagawa. Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5(1):1-25, March 1996.
38. M. Klass, N. de Freitas and A. Doucet. Towards practical n2 Monte Carlo: The marginal particle filter. In Proceedings of Uncertainty in Artificial Intelligence, 2005.
39. J. S. Liu and R. Chen. Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association,(443):1032-1044, 1998.
40. H. F. Lopes and C. M. Carvalho. Factor stochastic volatility with time varying loadings and Markov switching regimes. Journal of Statistical Planning and Inference, 137:3082-3091,2007.
41. R. P. S. Mahler. Multitarget Bayes filtering via first-order multitarget moments. IEEE Transactions on Aerospace and Electronic Systems, pages 1152-1178, October 2003.
42. R. P. S. Mahler. Statistical Multisource-Multitarget Information Fusion. Artech House, 2007.
43. S. Nadarajah and S. Kotz. Mathematical properties of the multivariate t distribution. Acta Applicandae Mathematicae, 89:53-84, 2005.
44. M. K. Pitt and N. Shephard. Filtering via simulation: Auxiliary particle filters. Journal of the American Statistical Association,(446):590-599,1999.
45. M. K. Pitt and N. Shephard. Auxiliary variable based particle filters. In Doucet etal. [20], chapter 13, pages 273-293.
46. G. Poyiadjis, A. Doucet and S.S. Singh. Particle methods for optimal filter derivative: application to parameter estimation. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), volume 5, pages 925-928, 2005.
47. C. P. Robert and G. Casella. Monte Carlo Statistical Methods. Springer Texts in Statistics. Springer, 2004.
48. S. Saha, P. K. Mandal, Y. Boers, H. Driessen, and A. Bagchi. Gaussian proposal density using moment matching in SMC methods. Statistics and Computing, 19:203-208, 2009.
49. H. Sidenbladh. Multi-target particle filtering for the probability hypothesis density. In Proceedings of the International Conference on Information Fusion, Cairns, Australia, pages 800-806, 2003.
50. S. S. Singh, B.-N. Vo, A. Baddeley and S. Zuyev. Filters for spatial point processes. Siam Journal on Control and Optimization, 48(4):2275-2295, 2008.
51. M. K. P. So, K. Lam and W. K. Li. A stochastic volatility model with Markov switching. Journal ofBusiness and Economic Statistics, 16(2):244-253, 1998.
52. B. Vo, S. Singh and A. Doucet. Sequential Monte Carlo methods for multitarget filtering with random finite sets. IEEE Transactions on Aerospace and Electronic Systems, 41(4):1224-1245, 2005.
53. N. Whiteley, A. M. Johansen and S. Godsill. Monte Carlo filtering of piecewise-deterministic processes. Journal of Computational and Graphical Statistics, 2011. To appear.
54. N. Whiteley, S. Singh and S. Godsill. Auxiliary particle implementation of the probability hypothesis density filter. IEEE Transactions on Aerospace and Electronic Systems, 46(3):1437-1454, 2010.
55. T. Zajic and R. P. S. Mahler. Particle-systems implementation of the PHD multitarget tracking filter. In Proceedings of SPIE, pages 291-299, 2003.