The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Variational Learning for Switching State-Space Models
Abstract
We introduce a new statistical model for time series that 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 neural network (Jacobs, Jordan, Nowlan, & Hinton, 1991) to its fully dynamical version, in which both expert and gating networks are recurrent. Inferring the posterior probabilities of the hidden states of this model is computationally intractable, and therefore the exact expectation maximization (EM) algorithm cannot be applied. However, we present a variational approximation that maximizes a lower bound on the log-likelihood and makes use of both the forward and backward recursions for hidden Markov models and the Kalman filter recursions for linear dynamical systems. We tested the algorithm on artificial data sets and a natural data set of respiration force from a patient with sleep apnea. The results suggest that variational approximations are a viable method for inference and learning in switching state-space models.
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Cites
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Self-citationAn introduction to hidden Markov models
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A Unifying Review of Linear Gaussian Models
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Self-citationSimultaneous Localization, Mapping and Moving Object Tracking
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Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures
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Metadata
- Authors
- Ghahramani, Z., & Hinton, G. E.
- Venue
- Neural Computation
- Publisher
- MIT Press
- Published
- 2000-04-01
- Volume
- 12
- Issue
- 4
- Pages
- 831-864
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2102716594
- Cited by
- 422
- References
- 47
- FWCI
- 12.49
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
