| Time Series and Sequential Data
|
Modelling time series and sequential data is an essential part of many
different areas of science and engineering, including for example,
signal processing and control, bioinformatics, speech recognition,
econometrics and finance. Using basic building blocks such as hidden
Markov models, linear Gaussian state-space models, and Bayesian
networks, it is possible to develop sophisticated time series models
for real world data. However learning (parameter inference / system
identification) becomes computationally challenging for such
sophisticated models.
See also Gaussian processes
Some relevant publications:
- van Gael, J., Saatci, Y., Teh, Y.-W., and Ghahramani, Z. (2008)
Beam sampling for the Infinite Hidden
Markov Model
Proceedings of the 25th International Conference on
Machine Learning (ICML-2008).
- Zhu, X., Ghahramani, Z., and Lafferty, J. (2005)
Time-Sensitive Dirichlet Process
Mixture Models.
Carnegie Mellon University Technical
Report CMU-CALD-05-104.
- Zhang, J., Ghahramani, Z. and Yang, Y. (2005)
A Probabilistic Model for
Online Document Clustering with Application to Novelty
Detection [ps]. [pdf]
In Advances in
Neural Information
Processing Systems 17. (NIPS-2004)
-
Beal, M. J., Ghahramani, Z. and Rasmussen, C. E. (2002)
The
Infinite Hidden Markov Model [pdf] [ps]
[abstract]
In Dietterich, T.G., Becker, S. and Ghahramani, Z. (eds)
Neural Information Processing Systems 14: 577-585. Cambridge,
MA, MIT Press.
- Ghahramani, Z. (2001)
An Introduction to Hidden Markov Models and Bayesian Networks [ps] [pdf]
International Journal of Pattern Recognition and
Artificial Intelligence 15(1):9-42.
-
Roweis, S. and Ghahramani, Z. (2000)
An EM Algorithm for Identification of Nonlinear Dynamical Systems [ps] [pdf]
Preprint.
- Ghahramani, Z. and Roweis, S. (1999)
Learning nonlinear dynamical systems
using an EM algorithm.
In M. S. Kearns, S. A. Solla,
D. A. Cohn, (eds.) Advances in Neural Information Processing
Systems 11:599-605. MIT Press.
-
Ghahramani, Z. and Hinton, G.E. (1998)
Variational learning for switching state-space models
Neural Computation, 12(4):963-996. [abstract]
-
Ghahramani, Z. (1998)
Learning Dynamic Bayesian Networks
In C.L. Giles and M. Gori (eds.), Adaptive Processing
of Sequences and Data Structures . Lecture Notes in Artificial
Intelligence, 168-197. Berlin: Springer-Verlag. [abstract]
- Ghahramani, Z. and Jordan, M.I. (1997)
Factorial Hidden Markov Models
Machine
Learning 29: 245-273. [abstract]
Software written in Matlab.
- Jordan, M.I, Ghahramani, Z. and Saul, L.K. (1997)
Hidden Markov Decision Trees
In
Advances in Neural Information Processing Systems 9,
7 pages.
- Ghahramani, Z. and Hinton, G.E. (1996)
Parameter estimation for
linear dynamical systems
University of Toronto Technical Report
CRG-TR-96-2, 6 pages (short note).