Dirichlet and beta processes are useful as Bayesian nonparametric priors for cluster and featural models. However, the infinite dimensionality of these priors does not immediately lead to straightforward inference algorithms, and therefore equivalent, flexible representations are sought. In this talk, I will review such representations, including a recently derived stick-breaking construction of...