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