Bayesian reasoning for data science. How to formulate and implement inference using the prior-to-posterior paradigm.
Rendered lecture slides: available here
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
| # | 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 |
- Probabilistic Programming and Bayesian Methods for Hackers (MDS alumni recommended!)
- Introduction to Empirical Bayes: Examples from Baseball Statistics
- Statistical Rethinking: A Bayesian Course with Examples in R and Stan plus exercises converted to PyMC3
- Bayesian Data Analysis book.
- JAGS with R
- A nice blog post summarizing how to write a JAGS model and input it into R (with the
rjagspackage).
- A nice blog post summarizing how to write a JAGS model and input it into R (with the
- Quora: For a non-expert, what is the difference between Bayesian and frequentist approaches?