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.
By the end of the course, students will be able to:
- Use Bayesian reasoning when modeling data.
- Apply Bayesian statistics to regression models.
- Compare and contrast Bayesian and frequentist methods, and evaluate their relative strengths.
- Use appropriate statistical libraries and packages for performing Bayesian inference (e.g., PyMC).
- Avoid the pitfalls of multiple comparisons by using the proper corrections.
- Gelman, Andrew and Carlin, John B. Bayesian Data Analysis, 3rd Edition. Chapman and Hall, 2013.
Note: information on this page is preliminary and subject to change.