8c19f571e251e61cb8dd3612f26d5ecf-Reviews.html

In this paper the authors presented a sequential variational inference algorithm for Dirichlet process mixture models. The authors used a posterior characterization of normalized random measures with independent increments as the basis for a variational distribution that was then used on a sequential decomposition of the posterior. The algorithm was demonstrated on a Gaussian mixture model applied to real and synthetic data, and a non-conjugate DP mixture of Dirichlet distributions to cluster text documents. This is a nice paper that aims to make a particular Bayesian nonparametric model useful to analyze massive data sets. The overall presentation is clear and well-motivated.