Abstract
Bayesian nonparametric (BNP) models play an important role in data sciences. However tutorials found online tend to be abstract and complex. Academic papers are often written for experts. Key ideas are assumed understood or explained at a level not easily accessible to non-technicians. This tutorial primary aim is to make BNP accessible to a broad audience of non-experts using the Dirichlet Process (DP) mixture model as an illustrative example. This workshop will also be useful to more experienced data scientists perhaps as a refresher course. This tutorial will: 1) explain what the DP and CRP look like; 2) explain the essential mathematical derivations often omitted in existing expositions you find online; and 3) demonstrate how to write a simple program in the statistical language R to fit a DP mixture model (DPMM). The mathematics will be no more than basic conditional probability and sampling from standard probability distributions.

Presenter
Yuelin Li