  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.
