Parameter Estimation and Interpretability in Bayesian Mixture Models

Explore the intricacies of parameter estimation and interpretability in Bayesian mixture models through this comprehensive seminar series. Delve into the research of Long Nguyen, an associate professor at the University of Michigan, as he examines posterior contraction behaviors for parameters in Bayesian mixture modeling. Investigate two types of prior specification: one with an explicit prior distribution on the number of mixture components, and another placing a nonparametric prior on the space of mixing distributions. Learn how these approaches yield optimal rates of posterior contraction and consistently recover unknown numbers of mixture components. Analyze the impact of model misspecification on posterior contraction rates, with a focus on the crucial role of kernel density function choices. Gain insights into the tradeoffs between model expressiveness and interpretability in mixture modeling, equipping yourself with valuable knowledge for statistical modeling in various applications.