Nonparametric Bayesian Methods: Models, Algorithms, and Applications

This tutorial took place as a primer as part of the Models, Inference, and Algorithms (MIA) series at the Broad Institute at MIT. See this link for the latest versions and videos of this tutorial.

Wednesday, May 17, 2017
8:30–9:30 AM

  Professor Tamara Broderick


Nonparametric Bayesian methods make use of infinite-dimensional mathematical structures to allow the practitioner to learn more from their data as the size of their data set grows. What does that mean, and how does it work in practice? In this tutorial, we'll cover why machine learning and statistics need more than just parametric Bayesian inference. We'll introduce and study a foundational nonparametric Bayesian model known as the Dirichlet process. Along the way, we'll see what exactly nonparametric Bayesian methods are and what they accomplish.