RECOMMENDED FOR ACCEPTANCE

Appropriate tools for managing large-scale data, like online texts, images and user pro-files, are becoming increasingly important. Hierarchical Bayesian models provide a natural framework for building these tools due to their flexibility in modeling real-world data. In this thesis, we describe a suite of efficient inference algorithms and novel models under the hierarchical Bayesian modeling framework. We first present a novel online inference algorithm for the hierarchical Dirichlet pro-cess. The hierarchical Dirichlet process (HDP) is a Bayesian nonparametric model that can be used to model mixed-membership data with a potentially infinite number of com-ponents. Our online variational inference algorithm is easily applicable to massive and streaming data and significantly faster than traditional inference algorithms. Second, we present a generic approximation framework for variational inference in a large family of nonconjugate models. For example, this includes multi-level logistic regression/generalized linear models and correlated topic models. With this, developing variational inference algorithm for many nonconjugate models is much easier