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Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models: a Variational Approach

2007

Technical Report

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We describe two related models to cluster multidimensional time-series under the assumption of an underlying linear Gaussian dynamical process. In the first model, times-series are assigned to the same cluster when they show global similarity in their dynamics, while in the second model times-series are assigned to the same cluster when they show simultaneous similarity. Both models are based on Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models in order to (semi) automatically determine an appropriate number of components in the mixture, and to additionally bias the components to a parsimonious parameterization. The resulting models are formally intractable and to deal with this we describe a deterministic approximation based on a novel implementation of Variational Bayes.

Author(s): Chiappa, S. and Barber, D.
Number (issue): 161
Year: 2007
Month: March
Day: 0

Department(s): Empirical Inference
Bibtex Type: Technical Report (techreport)

Institution: Max Planck Institute for Biological Cybernetics, Tübingen, Germany

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@techreport{4917,
  title = {Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models: a Variational Approach},
  author = {Chiappa, S. and Barber, D.},
  number = {161},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics, Tübingen, Germany},
  school = {Biologische Kybernetik},
  month = mar,
  year = {2007},
  month_numeric = {3}
}