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Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models: a Variational Approach
| Content Provider | Semantic Scholar |
|---|---|
| Author | Chiappa, Silvia |
| Copyright Year | 2007 |
| Abstract | 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, by biasing 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://is.tuebingen.mpg.de/fileadmin/user_upload/files/publications/silviachiappa07dirichlet_4917%5B0%5D.pdf |
| Alternate Webpage(s) | http://www.kyb.tuebingen.mpg.de/publications/attachments/silviachiappa07dirichlet_4917%5B0%5D.pdf |
| Alternate Webpage(s) | http://silviachiappa.swisspowered.net/publications/silviachiappa07dirichlet.pdf |
| Alternate Webpage(s) | http://web4.cs.ucl.ac.uk/staff/D.Barber/publications/silviachiappa07dirichlet.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |