Nonlinear Dynamical Systems Journal Club

An EM Algorithm for Identification of Nonlinear Dynamical Systems
Roweis, S. and Ghahramani, Z. (2000)

Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models
R. van der Merwe and E. Wan
Proceedings of the Workshop on Advances in Machine Learning, Montreal, Canada., Jun, 2003

Fisher scoring and a mixture of modes approach for approximate inference and learning in nonlinear state space models
T. Briegel and V. Tresp
In M. S. Kearns, S. A. Solla, and D. A. Cohn, editors, Advances in Neural Information Processing Systems 11. MIT Press, 1999.

Inferring state sequences for non-linear systems with embedded hidden Markov models
Neal, R. M., Beal, M. J., and Roweis, S.T. (2004),
in S. Thrun, et al (editors), NIPS*2003, MIT Press

On Sequential Monte Carlo Sampling Methods for Bayesian Filtering
A. Doucet, S.J. Godsill, C. Andrieu
Statistics and Computing, vol. 10, no. 3, pp. 197-208, 2000

Monte Carlo smoothing for non-linear time series
S.J. Godsill, A. Doucet, and M. West
Journal of the American Statistical Association, vol. 99, no. 465, March 2004.

A smoothing filter for Condensation
M. Isard and A. Blake
Proc 5th European Conf. Computer Vision, vol. 1 767-781, (1998).

Pampas: Real-Valued Graphical Models for Computer Vision
M. Isard
Proc. Computer Vision and Pattern Recognition, vol. 1 613-620. (2003)

Nonparametric Belief Propagation
E. Sudderth, A. Ihler, W. Freeman, and A. Willsky
IEEE Conference on Computer Vision and Pattern Recognition, June 2003

An Unsupervised Ensemble Learning Method for Nonlinear Dynamic State-Space Models
H. Valpola and J. Karhunen (2002)
Neural Computation 14(11), pp. 2647-2692, MIT Press.

Parameter Estimation in General State-Space Models using Particle Methods
A. Doucet and V.B. Tadic
Ann. Inst. Stat. Math., vol. 55, no. 2, pp. 409-422, 2003.

Online Expectation-Maximization Type Algorithms for Parameter Estimation in General State Space Models
C. Andrieu and A. Doucet
Proc. IEEE ICASSP 2003

To do:

Unscented Filtering and Nonlinear Estimation
S. J. Julier and J. K. Uhlmann
IEEE Review, vol. 92, no. 3, March 2004.

Smoothing Algorithms for State-Space Models
Mark Briers and Arnaud Doucet and Simon Maskell
submitted, IEEE Transactions on Signal Processing

Sequential Monte Carlo methods to train neural network models
de Freitas, JFG; Niranjan, M; Gee, AH; Doucet, A
Source: Neural Computation; April 2000; vol.12, no.4, p.955-93

Dynamic Learning With the EM Algorithm for Neural Networks
Nando de Freitas, Mahesan Niranjan and Andrew Gee
VLSI Signal Processing Systems. Pages 119--131. June 2000.

Learning multi-class dynamics
Andrew Blake, Ben North and Michael Isard.
Advances in Neural Information Processing Systems 11, 389-395, MIT Press, (1999).

Learning dynamical models by Expectation Maximisation.
Ben North and Andrew Blake
Proc 6th Int. Conf. Computer Vision, 384-389, (1998).

Learning and classification of complex dynamics
North, B.; Blake, A.; Isard, M.; Rittscher, J.
Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol.22, Iss.9, Sep 2000

Combined parameter and state estimation in simulation-based filtering
J. Liu and M. West
In Sequential Monte Carlo Methods in Practice (eds: A. Doucet et al), Springer-Verlag, 2000.

Gaussian sum particle filtering
Kotecha, J.H.; Djuric, P.M.
IEEE Transactions on Signal Processing, Vol.51, Iss.10, Oct. 2003

Estimating a state-space model from point process observations
Smith, AC; Brown, EN
Source: Neural Computation; May 2003; vol.15, no.5, p.965-91

Last Updated: December 3, 2004