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Abstract: We introduce a new statistical model for time series which iteratively segments data into regimes with approximately linear dynamics and learns the parameters of each of these linear regimes. This model combines and generalizes two of the most widely used stochastic time series models -- hidden Markov models and linear dynamical systems -- and is closely related to models that are widely used in the control and econometrics literatures. It can also be derived by extending the mixture of experts ... (Update)
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BibTeX entry: (Update)
Zoubin Ghahramani and Geo rey E. Hinton, \Variational learning for switching state-space models," Neural Computation, vol. 12, no. 4, pp. 831-864, 2000. http://citeseer.ist.psu.edu/article/ghahramani00variational.html More
@article{ ghahramani00variational,
author = "Zoubin Ghahramani and Geoffrey E. Hinton",
title = "Variational Learning for Switching State-Space Models",
journal = "Neural Computation",
volume = "12",
number = "4",
pages = "831--864",
year = "2000",
url = "citeseer.ist.psu.edu/article/ghahramani00variational.html" }
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