Dirichlet process mixture models provide a °exible Bayesian framework for estimating a distribution as an in¯nite mixture of simpler distributions that could identify latent classes in the data [1]. However the full exchangeability assumption they employ makes them an unappealing choice for modeling longitudinal data such as text, audio and video streams that can arrive or accumulate as epochs, where data points inside the same epoch can be assumed to be fully exchangeable, whereas across the epochs both the structure (i.e., the number of mixture components) and the parameteriza- tions of the data distributions can evolve and therefore unexchangeable.