* Tutorial materials can be found here.
* Registration is now open! Please follow this link.
Nonparametric Bayesian methods are data analytic tools applicable to students in a variety of disciplines. 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. This tutorial, will cover why machine learning and statistics need more than just parametric Bayesian inference and will introduce foundational nonparametric Bayesian models as the Dirichlet process and Chinese restaurant process and touch on the wide variety of models available in nonparametric Bayes. It will show exactly what nonparametric Bayesian methods are and what they accomplish.
The workshop will take place on May 30th to June 1st. We will have one tutorial session and one work session each day. Tentative schedule is as follows:
10:30am-12:00pm: Tutorial Session (Friend Center 006)
12:00pm-01:00pm: Lunch (Friend Center Convocation Room)
01:00pm-02:30pm: Work Session (Friend Center Convocation Room)
We are holding an informal poster session on June 1st during the afternoon work session. Feel free to bring an old conference poster, print out your slides, or bring out your artistic side through hand drawn images. This is an opportunity to present your work, network with your peers, and get feedback on how you can incorporate nonparametric methods into your own research.
No abstract necessary! If you would like to share your current work and discuss how you can apply the tools you learned in the workshop, indicate your interest here.