Resources
Here are some resources you might find useful for learning more about Bayesian
statistics. This list is by no means comprehensive, but hopefully will be
useful. As a shameless plug, ee’ve also included a couple of articles that
attempt to combine Bayesian analyses and critical frameworks.
Books
- McElreath, R. (2018). Statistical rethinking: A Bayesian course with examples
in R and Stan. Chapman and Hall/CRC.
- Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin,
D. B. (2014). Bayesian Data Analysis. CRC Press, 3rd edition.
Articles
- Gelman, A., Vehtari, A., Simpson, D., Margossian, C. C., Carpenter, B., Yao,
Y., Kennedy, L., Gabry, J., Burkner, P-C, & Modrák, M. (2020). Bayesian
workflow. arXiv preprint
- Skinner, B. T., Levy, H., and Burtch, T. (2023). Digital redlining: the
relevance of 20th century housing policy to 21st century broadband access and
education. Educational
Policy. CODE
- Skinner, B. T., Burtch, T., & Levy, H. (2022). Variation in broadband access
among undergraduate populations across the United
States. Annenberg Institute at
Brown University, (EdWorkingPaper: 22-667).
CODE
Software
R
- Stan: A modern statistical programming language for
performing Bayesian analyses
- brms: An R package for Bayesian
modeling based on the lme4 syntax that uses Stan under the hood
- ShinyStan: Interactive tool
for the inspection of Bayesian models
Python
- PyMC: An alternative to Stan that works in Python
Visualizations