anhnguyendepocen / ecots2022_workshop

eCOTS 2022 Workshop 4

Introducing Bayesian Analysis into Your Teaching

Kevin Ross and Jingchen (Monika) Hu

May 19, 2022

Additional discussion questions

Section 1: Curriculum & content

1. What is the status of (Bayesian) statistics curriculum in your department / institution?
2. Are you interested in introducing / developing Bayesian ideas into your curriculum? Why?
3. Do you have any previous experience with teaching Bayesian ideas? At what level?
4. Are you interested in designing a Bayesian module or a course (or both)?
5. If you are designing a Bayesian module, what courses do you plan to add it into? What topics will you cover?
6. If you are designing a Bayesian course, what level(s) do you plan for? Intro, intermediate, or advanced?
7. What is the student body that the module / course will serve?
8. What are the pre-requisities for your module / course?
9. What role does calculus / probability play in your module / course?
10. Do you plan to compare Bayesian and frequentist methods? Why? How?
11. Do you plan to include a project component in your course? Why or why not?
12. What are the content challenges you foresee, especially given what’s presented in the workshop?

Section 2: Computing

1. How much computing do you expect to require of students?
2. What Bayesian computing resources are you familiar with? What are their appealing features?
3. Do you plan to ask students to code their own MCMC algorithms (Gibbs, Metropolis, or Metropolis-Hastings)? Why or why not?
4. What are the computing challenges you foresee, especially given what’s presented in the workshop?

Section 3: Resources

1. What resources for teaching and learning Bayesian ideas are you familiar with? Please share!
2. What resources do you think you will use from the workshop?
3. What resources do you wish were available?

Links to resources

• A GitHub repo on Vassar’s Bayesian Statistics course material (lectures, labs, homework, cases studies etc.)
• A GitHub repo on various resources for undergraduate Bayesian education
• The Undergraduate Bayesian Education Network with resources and an associated Slack channel for you to join!