ST 559 - S 26
Syllabus
Much of the material for this course was inspired/cribbed from Aki Vehtari’s Bayesian course at Aalto https://avehtari.github.io/BDA_course_Aalto/Aalto2025.html, and Ben Goodrich’s Bayesian course at Columbia https://github.com/bgoodri/GR5065_2024.
Instructor Info
| Role | Name | Office hours | Contact |
|---|---|---|---|
| Instructor | Rob Trangucci | Tues 3p-4p WNGR 207 | rob.trangucci@oregonstate.edu |
| GTA | Gauri Phatak | TBD | phatakg@oregonstate.edu |
Class meetings
| Activity | Days | Time | Room |
|---|---|---|---|
| Lecture | TR | 10a-11:20a | KEAR 124 |
All lectures are in class and attendance is mandatory
Prerequisites
ST 562 is the recommended prerequisite. This course is going to delve headfirst into comparisons between Bayesian and frequentist inference, so you should be fairly familiar with probability distributions, expectation, variance, Bayes’ rule.
Textbook:
Required: Bayesian Data Analysis, Third Ediction, Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin
Freely available here: https://sites.stat.columbia.edu/gelman/book/
Course objectives
To introduce students to Bayesian statistics through practical examples and computation. To apply these techniques to real data using R code and Stan code. To understand the theory and methodology of Bayesian inference through math, practice and code.
Course content
Concepts to be discussed include: Bayesian vs. frequentist inference, prior and posterior distributions, prior predictive and posterior predictive distributions, exchangeability, hierarchial models, modern Bayesian computation via Markov Chain Monte Carlo, modern probabilistic programming languages.
Course learning outcomes
- Differentiate between Bayesian inference and frequentist inference via quantification of uncertainty.
- Derive conjugate priors for a given likelihood family.
- Calculate and interpret MCMC and HMC diagnostic checks.
- Define and perform inference for basic hierarchical models in Stan.
- Perform prior and posterior predictive checks while building models.
Rough schedule and readings
| Date | Topic | Reading | LO | Assignments |
|---|---|---|---|---|
| 3/31/2026 | Introduction to Bayesian Inference | Chapter 1 | 1 | NA |
| 4/2/2026 | Single parameter models | Chapter 2 | 1, 2 | NA |
| 4/7/2026 | Single parameter models | Chapter 2 | 1, 2 | HW 1 |
| 4/9/2026 | Multi parameter models | Chapter 3 | 1, 2 | NA |
| 4/14/2026 | Multi parameter models | Chapter 3 | 1, 2 | NA |
| 4/16/2026 | Multivariate normal models | Chapters 3, 10 | 1, 2 | HW 2 |
| 4/21/2026 | Monte Carlo Integration | Chapter 10 | 1, 2 | NA |
| 4/23/2026 | MCMC | Chapter 11 | 3 | NA |
| 4/28/2026 | Stan/HMC/PPL | Chapter 11 | 3 | NA |
| 4/30/2026 | Stan/HMC/PPL | Chapter 12 | 3-4 | NA |
| 5/5/2026 | Stan/HMC/PPL | Chapter 12 | 3-4 | NA |
| 5/7/2026 | Stan/HMC/PPL | Chapter 12 | 3-4 | NA |
| 5/12/2026 | Hierarchical Models | Chapter 5 | 4 | NA |
| 5/14/2026 | Hierarchical Models | Chapter 5 | 4 | NA |
| 5/19/2026 | Model checking | Chapter 6 | 5 | NA |
| 5/21/2026 | Bayesian Workflow | Chapter 6 | 4-5 | NA |
| 5/26/2026 | Bayesian Workflow | Chapter 6 | 4-5 | NA |
| 5/28/2026 | Decision analysis | Chapter 9 | 4-5 | NA |
| 6/2/2026 | Presentations | NA | 1-5 | NA |
| 6/4/2026 | Presentations | NA | 1-5 | NA |
Project proposal
Course notes
Homework
Assessments
The course will have five homework assignments, a final project proposal, a final project presentation and a project report. The breakdown is
| Assessment | Weight |
|---|---|
| HW | 50% |
| Proposal | 5% |
| Presentation | 10% |
| Report | 35% |
Grading scale
The tentative grading scale is below:
| Range | Letter Grade |
|---|---|
| 93% - 100% | A |
| 90% - 92.9% | A- |
| 87% - 89.9% | B+ |
| 83% - 86.9% | B |
| 80% - 82.9% | B- |
| 77% - 79.9% | C+ |
| 73% - 76.9% | C |
| 70% - 72.9% | C- |
| 67% - 69.9% | D+ |
| 63% - 66.9% | D |
| 60% - 62.9% | D- |
| 0% - 59.9% | F |
Course Statements
Academic Calendar
All students are subject to the registration and refund deadlines as stated in the Academic Calendar: https://registrar.oregonstate.edu/osu-academic-calendar
Statement Regarding Students with Disabilities
Accommodations for students with disabilities are determined and approved by Disability Access Services (DAS). If you, as a student, believe you are eligible for accommodations but have not obtained approval please contact DAS immediately at 541-737-4098 or at http://ds.oregonstate.edu. DAS notifies students and faculty members of approved academic accommodations and coordinates implementation of those accommodations. While not required, students and faculty members are encouraged to discuss details of the implementation of individual accommodations.”
Student Conduct Expectations
Student Bill of Rights
OSU has twelve established student rights. They include due process in all university disciplinary processes, an equal opportunity to learn, and grading in accordance with the course syllabus: https://asosu.oregonstate.edu/advocacy/rights
Reach Out for Success
University students encounter setbacks from time to time. If you encounter difficulties and need assistance, it’s important to reach out. Consider discussing the situation with an instructor or academic advisor. Learn about resources that assist with wellness and academic success at https://oregonstate.edu/reachout. If you are in immediate crisis, please contact the Crisis Text Line by texting OREGON to 741-741 or call the National Suicide Prevention Lifeline at 1-800-273-TALK (8255)
Student Evaluation of Courses
During Fall, Winter, and Spring term the online Student Learning Experience surveys open to students the Wednesday of week 9 and close the Sunday before Finals Week. Students will receive notification, instructions and the link through their ONID email. They may also log into the system via MyOregonState or directly at https://beaves.es/Student-Learning-Survey. Survey results are extremely important and are used to help improve courses and the learning experience of future students. Responses are anonymous (unless a student chooses to “sign” their comments, agreeing to relinquish anonymity of written comments) and are not available to instructors until after grades have been posted. The results of scaled questions and signed comments go to both the instructor and their unit head/supervisor. Anonymous (unsigned) comments go to the instructor only.