The talk investigates the usefulness of combining stationary time series models with Bayesian nonparametric, i.e. infinite mixture, transition and stationary density. It is argued that such a combination has the capacity to model data arising from any time homogeneous transition density function, whether it possesses a stationary density or not. Illustrations will be presented.