Parsimonious Hierarchical Modeling Using Repulsive Distributions

Understand

Employing nonparametric methods for density estimation has become routine in Bayesian statistical practice.

• Models based on discrete nonparametric priors such as Dirichlet Process Mixture (DPM) models are very attractive choices due to their flexibility and tractability.
• However, a common problem in fitting DPMs or other discrete models to data is that they tend to produce a large number of (sometimes) redundant clusters.
• In this work we propose a method that produces parsimonious mixture models (i.e.