  Using nonparametric methods has been increasingly explored in Bayesian
hierarchical modeling as a way to increase model flexibility. Although the
field shows a lot of promise, inference in many models, including Hierachical
Dirichlet Processes (HDP), remain prohibitively slow. One promising path
forward is to exploit the submodularity inherent in Indian Buffet Process (IBP)
to derive near-optimal solutions in polynomial time. In this work, I will
present a brief tutorial on Bayesian nonparametric methods, especially as they
are applied to topic modeling. I will show a comparison between different
non-parametric models and the current state-of-the-art parametric model, Latent
Dirichlet Allocation (LDA).
