ORIE 6217 - Applied Bayesian Analysis for Computational Research

(crosslisted) CS 6384

Spring. 3 credits. Student option grading.

Recommended prerequisite: some coursework in mathematical maturity as well as probability statistics. Enrollment limited to: Cornell Tech students and Ithaca PhD Students. Offered in New York City at Cornell Tech.

N. Garg.

Bayesian modeling and data analysis is a powerful tool for computational research. It consists of writing a probability model and then fitting it with observed data, while handling uncertainty. The model can be flexible, encompassing hierarchy, spatio-temporal dynamics, graphs, and high-dimensionality. This course is a graduate, hands-on introduction to Bayesian analysis in Stan and/or Pyro. The focus will be on writing and fitting models in practice for computational research, including the applied Bayesian statistics workflow: model building, checking, and evaluation. The course will also discuss research papers that use such methods.

Outcome 1: Students will start with a research question and construct a data generating process for the setting then construct a Bayesian model reflecting that process.

Outcome 2: Students will record the model in a Bayesian programming language such as Stan and/or Pyro.