Short Description

Methods for dealing with the multiple testing problem. Bayesian reasoning for data science. How to formulate and implement inference using the prior-to-posterior paradigm.

Learning Outcomes

By the end of the course, students will be able to:

  1. Use Bayesian reasoning when modeling data.
  2. Apply Bayesian statistics to regression models.
  3. Compare and contrast Bayesian and frequentist methods, and evaluate their relative strengths.
  4. Use appropriate statistical libraries and packages for performing Bayesian inference (e.g., PyMC).
  5. Avoid the pitfalls of multiple comparisons by using the proper corrections.

Reference Material

  • Gelman, Andrew and Carlin, John B. Bayesian Data Analysis, 3rd Edition. Chapman and Hall, 2013.

Instructor (2016-2017)

Note: information on this page is preliminary and subject to change.