Software choices for
Bayes learners

Day 5

What software do you use?

  • For research

  • For preparing your teaching materials

  • For teaching your student (i.e. the software that your students learn)

  • Any software specifically for Bayesian methods?

Small Group Discussion

Format and Length

  • About 30 minutes

  • 4 small groups, each led by one Instructor/TA

  • Random assignment; get to know people from a different discipline!

  • Assign a note-taker; if okay, please share notes with us afterwards

  • Assign a reporter for large group sharing (3 minutes per group)

  • Feel free to use our prepared questions and feel free to come up with your own

Discussion Questions

We have seen rstan, rstanarm, and brms packages for fitting models.

  1. Which one of these packages would you use for your own research?

  2. If you were to teach with any of these packages to your students, which one would you consider using and why?

Discussion Questions

  1. We have also used R packages not for fitting Bayesian models but to support learning of through data visualizations. We have utilized some R functions such as bayesrules::plot_beta_binomial() and bayesplot::ppc_intervals() to get these visualizations. How would these data visualizations tools support learning for your students? What are the pros and cons of using these tools?

Discussion Questions

  1. Do you use any other software/packages that you think can support learning of Bayesian methods?

Large Group Sharing

Format and Length

  • About 25 minutes

  • We will record this portion for sharing later. Okay?

  • Each group reports back for 3 minutes

  • Open discussion for remaining time

Note that Monika and I discuss what we do in our courses that may or may not be applicable for your students in Section 3.2 of Content and computing outline of two undergraduate Bayesian courses: Tools, examples, and recommendations. You can also see another language JAGS and side-by-side comparison of JAGS and Stan.

In addition Jim Albert and Monika discuss Bayesian Computing in the Undergraduate Statistics Curriculum.