Uncertainty in Artificial Intelligence
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Generalized Polya Urn for Time-varying Dirichlet Process Mixtures
Francois Caron, Manuel Davy, Arnaud Doucet
Abstract:
Dirichlet Process Mixtures (DPMs) are a popular class of statistical models to perform density estimation and clustering. However, when the data available have a distribution evolving over time, such models are inade- quate. We introduce here a class of time- varying DPMs which ensures that at each time step the random distribution follows a DPM model. Our model relies on an intuitive and simple generalized Polya urn scheme. Inference is performed using Markov chain Monte Carlo and Sequential Monte Carlo. We demonstrate our model on various appli- cations.
Keywords:
Pages: 33-40
PS Link:
PDF Link: /papers/07/p33-caron.pdf
BibTex:
@INPROCEEDINGS{Caron07,
AUTHOR = "Francois Caron and Manuel Davy and Arnaud Doucet",
TITLE = "Generalized Polya Urn for Time-varying Dirichlet Process Mixtures",
BOOKTITLE = "Proceedings of the Twenty-Third Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-07)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2007",
PAGES = "33--40"
}


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