CS 6384

Last Updated

  • Schedule of Classes - April 4, 2023 12:09PM EDT
  • Course Catalog - April 3, 2023 12:59PM EDT

CS 6384
Applied Bayesian Analysis for Computational Research

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.

Outcomes
  • 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

Syllabi: none

  •   Regular Academic Session.  Combined with: ORIE 6217

  • 3 Credits Stdnt Opt

  • 19735 CS 6384   LEC 001

  •  

  • Instruction Mode: Distance Learning-Synchronous

Syllabi: none

  •   Regular Academic Session.  Combined with: ORIE 6217

  • 3 Credits Stdnt Opt

  • 19736 CS 6384   LEC 030

  •  

  • Instruction Mode: In Person
    Taught in NYC. Enrollment limited to Cornell Tech Students.