Expectation propagation for inference in non-linear dynamical models with poisson observations

NSSPW - Nonlinear Statistical Signal Processing Workshop 2006

Volumn , Issue , 2006, Pages 83-86

  Yu, Byron M ^a   Shenoy, Krishna V ^a,b   Sahani, Maneesh ^c  

^a Stanford University   (United States)

^b STANFORD UNIVERSITY   (United States)

^c UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DYNAMICAL SYSTEMS; ELECTROPHYSIOLOGY; LINEAR CONTROL SYSTEMS; NEURONS; STATE ESTIMATION;

ACTION POTENTIALS; EXPECTATION PROPAGATION; GAUSSIAN QUADRATURES; HIGH-DIMENSIONAL; NONLINEAR STATE; NONNEGATIVE INTEGERS; STATE ESTIMATORS; UNSCENTED KALMAN SMOOTHERS;

SIGNAL PROCESSING;

EID: 48049115018     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/NSSPW.2006.4378825     Document Type: Conference Paper

References (12)

1. B.M. Yu, A. Afshar, G. Santhanam, S.I. Ryu, K.V. Shenoy, and M. Sahani, "Extracting dynamical structure embedded in neural activity," in Advances in Neural Information Processing Systems 18, Y. Weiss, B. Schölkopf, and J. Platt, Eds. MIT Press, Cambridge, MA, 2006.
2. E. A. Wan and R. van der Merwe, "The unscented Kalman filter," in Kalman Filtering and Neural Networks, S. Haykin, Ed., chapter 7. Wiley Publishing, 2001.
3. M. Briers, A. Doucet, and S. Maskell, "Smoothing algorithms for state-space models," Tech. Rep. CUED/F-INFENG/TR.498, Cambridge University Engineering Department, Aug. 2004.
4. T. Heskes and O. Zoeter, "Expectation propagation for approximate inference in dynamic Bayesian networks," in Proceedings UAI-2002, A. Darwiche and N. Friedman, Eds., 2002, pp. 216-223.
5. O. Zoeter, A. Ypma, and T. Heskes, "Improved unscented Kalman smoothing for stock volatility estimation," in Proceedings of the IEEE Workshop on Machine Learning for Signal Processing, A. Barros, J. Principe, J. Larsen, T. Adali, and S. Douglas, Eds., 2004.
6. A. Ypma and T. Heskes, "Novel approximations for inference in nonlinear dynamical systems using expectation propagation," Neurocomputing, vol. 69, pp. 85-99, 2005.
7. T.P. Minka, A Family of Algorithms for Approximate Bayesian Inference, Ph.D. thesis, Massachusetts Institute of Technology, 2001.
8. U.N. Lerner, Hybrid Bayesian Networks for Reasoning about Complex Systems, Ph.D. thesis, Stanford University, 2002.
9. D. J. C. MacKay, Information Theory, Inference and Learning Algorithms, Cambridge University Press, 2003.
10. S. J. Julier and J. K. Uhlmann, "Unscented filtering and nonlinear estimation," Proceedings of the IEEE, vol. 92, no. 3, pp. 401-422, Mar. 2004.
11. T.P. Minka, "Deriving quadrature rules from Gaussian processes," Tech. Rep., 2000, http://research.microsoft.com/~minka/papers/ quadrature.html.
12. S. Julier, J. Uhlmann, and H.F. Durrant-Whyte, "A new method for the nonlinear transformation of means and covariances in filters and estimators," IEEE Transactions on Automatic Control, vol. 45, no. 3, pp, 477-482, Mar. 2000.