Generalized polya urn for time-varying dirichlet process mixtures

Journal article

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 inadequate. 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 Mo...

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Authors: Caron, F; Davy, M; Doucet, A

• Journal: Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007
• Pages: 33-40
• Publication date: 2007-01-01
• Language: English