Truncation-free Online Variational Inference for Bayesian Nonparametric Models

Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)

Bibtex Metadata Paper Supplemental


Chong Wang, David Blei


We present a truncation-free online variational inference algorithm for Bayesian nonparametric models. Unlike traditional (online) variational inference algorithms that require truncations for the model or the variational distribution, our method adapts model complexity on the fly. Our experiments for Dirichlet process mixture models and hierarchical Dirichlet process topic models on two large-scale data sets show better performance than previous online variational inference algorithms.