Particle filters for graphical models

NSSPW - Nonlinear Statistical Signal Processing Workshop 2006

Volumn , Issue , 2006, Pages 59-64

  Briers, M ^a,c   Doucet, A ^b   Singh, S S ^a   Weekes, K ^c  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^c QINETIQ   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; GRAPHIC METHODS; INFERENCE ENGINES; MONTE CARLO METHODS; OBJECT TRACKING;

ARTICULATED OBJECT TRACKING; COMPUTATIONAL COSTS; INFERENCE ALGORITHM; LOOPY BELIEF PROPAGATION; NON-GAUSSIAN MODELS; NON-LINEAR SIGNAL PROCESSING; NUMERICAL APPROXIMATIONS; SEQUENTIAL MONTE CARLO;

SIGNAL PROCESSING;

EID: 48049094154     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/NSSPW.2006.4378820     Document Type: Conference Paper

References (14)

1. J Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann Publishers, 1988.
2. K'Murphy, Y Weiss, and M Jordan, "Loopy-belief propagation for approximate inference: An empirical study," in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, July 1999.
3. M K Pitt and N Shephard, "Filtering via simulation: Auxiliary particle filters," Journal of the American Statistical Association, vol. 94, no. 446, pp. 590-599, 1999.
4. E B Sudderth, A Jhler, W Freeman, and A Willsky, "Nonparametric Belief Propagation," Tech. Rep. 2551, Massachusetts Institute of Technology Laboratory for Information and Decision Systems, October 2002.
5. A T Jhler, E Sudderth, W Freeman, and A S Willsky, "Efficient multiscale sampling from products of Gaussian mixtures," in NIPS, December 2003.
6. M Isard, "Pampas: Real-valued graphical models for computer vision," in Computer Vision and Pattern Recognition, 2003, vol. 1, pp. 613-620.
7. A T Jhler, Inference in Sensor Networks: Graphical Models and Particle Methods, Ph.D. thesis, Massachusetts Institute of Technology, 2005.
8. E B Sudderth, Graphical Models for Visual Object Recognition and Tracking, Ph.D. thesis, Massachusetts Institute of Technology, 2006.
9. C Berzuini, N G Best, W R Gilks, and C Larizza, "Dynamic conditional independence models and Markov chain Monte Carlo methods," Journal of the American Statistical Association, vol. 92, pp. 1403-1412, 1997.
10. R van der Merwe, A Doucet, J F G de Freitas, and E Wan, 'The Unscented particle filter," in Advances in Neural Information Processing Systems 13, T K. Leen, T G Dietterich, and V Tresp, Eds. 2000, MET Press.
11. C Andrieu, M Davy, and A Doucet, "Improved auxiliary particle filtering: Application to time-varying spectral analysis," in IEEE Workshop on Statistical Signal Processing, Singapore, 2001.
12. S J Julier and J K Uhlmann, "Unscented filtering and nonlinear estimation," Proceedings of the IEEE, vol. 92, no. 3, pp. 401-422, 2004.
13. M Briers, A Doucet, and S S Singh, "Sequential auxiliary particle belief propagation," in The 8th International Conference on Information Fusion, 2005.
14. L Sigal, M Isard, B H Sigelman, and M Black, "Attractive people: Assembling loose-limbed models using non-parametric belief propagation," in Advances in Neural Information Processing Systems, 2003.