/DSCI_553_stat-inf-2

DSCI 553: Statistical Inference and Computation II

Bayesian reasoning for data science. How to formulate and implement inference using the prior-to-posterior paradigm.

Rendered lecture slides: available here

Learning Objectives

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

Lecture Schedule

# Date Day Topic Reading
1 2019-02-05 Tues Bayesian tour in the discrete case (some probability recap at same time)
2 2019-02-07 Thurs More discrete Bayesian analysis
3 2019-02-12 Tues Debrief from last week's lab. Next: Bayes modelling 101 with PPL, including continuous spaces this time
4 2019-02-14 Thurs Practical issues, model bestiary
5 2019-02-26 Tues
6 2019-02-28 Thurs
7 2019-03-05 Tues Looking under the hood of PPLs (if time permits)
8 2019-03-07 Thurs Computation, continued

Reference Material