Time-varying Dirichlet process mixtures
By Francois Caron
Dirichlet Process Mixtures (DPM) 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.