ORIE 6217

ORIE 6217

Course information provided by the Courses of Study 2022-2023.

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.

When Offered Spring.

Permission Note Enrollment limited to: Cornell Tech students and Ithaca PhD Students only.
Prerequisites/Corequisites Highly recommended prerequisite: some coursework in mathematical maturity as well as probability statistics.

  • Students will start with a research question and construct a data generating process for the setting then construct a Bayesian model reflecting that process.
  • Students will record the model in a Bayesian programming language such as Stan and/or Pyro.

View Enrollment Information

  •   Regular Academic Session.  Combined with: CS 6384

  • 3 Credits Stdnt Opt

  • 19282 ORIE 6217   LEC 001

  • Instruction Mode: Distance Learning-Synchronous
    Taught in NYC. This class will be streamed from NYTech. Limited to Ph.D. students

  • 18994 ORIE 6217   LEC 030

  • Instruction Mode: In Person
    Taught in NYC. Enrollment Limited to Cornell Tech PhD Students. Master's Tech Enrollment allowed with instructor permission.