Iterated extended kalman smoothing with expectation-propagation

Neural Networks for Signal Processing - Proceedings of the IEEE Workshop

Volumn 2003-January, Issue , 2003, Pages 219-228

  Ypma, Alexander ^a   Heskes, Tom ^a  

^a SNN Nijmegen   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; DYNAMICAL SYSTEMS; LINEARIZATION; NONLINEAR DYNAMICAL SYSTEMS;

EXPECTATION PROPAGATION; GAUSSIAN FORM; HIGHER-ORDER APPROXIMATION; KAIMAN FILTER; KALMAN SMOOTHING; LOCAL LINEARIZATION; NON-GAUSSIAN; SECOND-ORDER APPROXIMATION;

SIGNAL PROCESSING;

EID: 77956897530     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/NNSP.2003.1318021     Document Type: Conference Paper

References (11)

1. A. P. Dempster, N. M. Laird, and D- B. Rubin, Maximum likelihood from incomplete data via the EM algorithm. Journal of Royal Statistica! Society, 39:1-38, 1977.
2. T. Heskes and O. Zoeter. Expectation propagation for approximate inference in dynamic Bayesian networks. In A. Daxwiche and N. Friedman, editors, Proceedings UAI-200S, pages 216-223, 2002
3. S. Julier and J. UbJmann. A new extension of the Kaiman filter to nonlinear systems. In Int. Symp. Aerospace/Defense Sensing, Simul. and Controls, Orlando, Florida, 1997.
4. F. R. Kschischang, D. Frey, and H.-A- Loeliger. Factor graphs and the sum-product algorithm. IEEE Trans. Inform. Theory, 47(2):498-519, 2001.
5. U. Lerner, B. Moses, M. Scott, S. Mcllraith, and D. Koller. Monitoring a complex physical system using a hybrid dynamic Bayes net. In Proceedings of the 18th Annual Conference on Uncertainty in Al (UAI'02), pages 301-310, 2002.
6. T. Minka. From hidden markov models to linear dynamical systems. Technical Report 531, Dept. of Electrical Engineering and Computer Science, MIT, 1999.
7. T. Minka. A family of algorithms for approximate Bayesian inference. PhD thesis, Dept. of Electrical Engineering and Computer Science, MIT, 2001.
8. K. Murphy. Dynamic Bayesian networks, chapter in Probabilistic Graphical Models, Jordan (ed.). publisher unknown, 2002.
9. S. Roweis and Z. Ghahramani. An EM algorithm for identification of nonlinear dynamical systems, submitted, 2001.
10. H. Valpola and J. Karhunen. An unsupervised ensemble learning method for nonlinear dynamic state-space models. Neural computation, 14(11):2647-2692, 2002.
11. E. Wan and R. van der Merwe. The unscented Kaiman ßter, chapter in Kaiman Filtering and Neural Networks, Haykin (ed.). Wiley, 2001.