W04: Teaching Bayesian Statistics at the Undergraduate Level

Mine Dogucu (University of California Irvine), Alicia Johnson (Macalester College), & Miles Ott (Smith College)


The popularity of Bayesian Statistics has outpaced the curriculum needed to support it. Even though Bayesian Statistics is a common course in graduate training in Statistics, it is not as common in undergraduate training. The workshop is intended for instructors who have taught frequentist statistics before and are interested in teaching a Bayesian Statistics course at the undergraduate level. In the workshop, we will introduce a course curriculum that we have taught in three different institutions at the undergraduate level with varying prerequisite courses.

Throughout the workshop we will cover building, simulating, and analyzing Bayesian models with hands-on teaching examples. We will introduce an accompanying open-access textbook and R package for the course. Participants will leave the workshop with ready-to-use materials for their own courses. Introductory level knowledge of regression is required and probability knowledge is suggested. Reading level proficiency in R and installation of R are required but those who are unfamiliar with R can still participate and learn about the curriculum. Participants can access instructions on installing R and the required packages as well as the materials at https://github.com/bayes-rules/uscots-2021 three days prior to the workshop.