arXiv · 1206.5254
Generalized Polya Urn for Time-varying Dirichlet Process Mixtures
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 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 Monte Carlo. We demonstrate our model on various applications.
Explore related subjects
Keep this discovery
Francois Caron, Manuel Davy, Arnaud Doucet. 2012-06-20. Generalized Polya Urn for Time-varying Dirichlet Process Mixtures. https://arxiv.org/abs/1206.5254
Cite the original work for its findings. Save a collection to share your selection of sources.