  Dirichlet process mixture models (DPMM) are a cornerstone of Bayesian
non-parametrics. While these models free from choosing the number of components
a-priori, computationally attractive variational inference often reintroduces
the need to do so, via a truncation on the variational distribution. In this
paper we present a truncation-free hybrid inference for DPMM, combining the
advantages of sampling-based MCMC and variational methods. The proposed
hybridization enables more efficient variational updates, while increasing
model complexity only if needed. We evaluate the properties of the hybrid
updates and their empirical performance in single- as well as mixed-membership
models. Our method is easy to implement and performs favorably compared to
existing schemas.
