Introduction to Bayesian statistics with brms and the tidyverse

⚠️ This workshop has already come and gone. You can find some of the original marketing descriptions, along with a link to the online supporting materials, below.

Overview

We are entering the Golden Age of Bayesian statistics. Thanks to fast personal computers and powerful free software (e.g., Stan), working scientists can fit an array of Bayesian models tailored to their specific needs. Recent textbooks from authors like McElreath (2015, 2020) have also made Bayesian methods more accessible to applied researchers with minimal backgrounds in mathematics. However, many graduate programs still do not offer introductory courses on Bayesian statistics. To help fill that pedagogical gap, this course is designed to provide an accessible and applied introduction to Bayesian data analysis for a wide variety of linear models using user-friendly brms R package.

Prerequisites

We assume familiarity with R, regression, and the Generalized Linear Model (e.g., logistic regression, Poisson regression). Participants will benefit most if they have some experience with multilevel models. No knowledge of calculus or linear algebra is assumed, but basic school level mathematics knowledge is assumed. Most of the R code will follow the tidyverse style.

Outcomes

After completing this course, the participants will:

1. have become familiar with the basics of Bayesian inference,
2. be able to fit a range of regression models with several likelihood functions,
3. be able fit several robust models and distributional models,
4. know how to select priors for their models using prior predictive checks,
5. know how to assess the descriptive accuracy of a model using posterior predictive checks, and
6. know how to express their posterior distributions as effect sizes and informative figures.

Background reading

This course will draw from several introductory textbooks, such as:

• Gelman et al (2020), Kruschke (2015), McElreath (2015, 2020), and Nicenboim et al (2022).
• I have released several free ebooks which translate other textbooks into brms and tidyverse-style code, which you can find here.
• For a conceptual introduction to Bayesian data analysis, check out the 2-hour lecture by McElreath, “ Bayesian Inference is Just Counting”.
• For basic R programming with tidyverse methods, we recommend the free ebook by Grolemund & Wickham (2017).
• For introductions to the brms package, we recommend the reference manual and the several vignettes listed on the brms CRAN page.

Supporting materials

You can find supporting materials from the workshop on the OSF at https://osf.io/bfvs4/.