Supplementary Textbooks

These textbooks are great resources for some of the topics we will cover. You do not need to buy them, but you may be able to borrow them from Duke library should you need extra reading materials, besides the class slides and main textbooks.

  1. Albert, J. (2009), "Bayesian Computation with R (Second Edition)."
  2. Bolstad, W. M. and Curran, J. M. (2016), "Introduction to Bayesian Statistics (Third Edition)."

R and R Markdown Resources

R Markdown can be used to create high quality reports and presentations with embedded chunks of R code. You are required to use R Markdown to type up your lab reports. R Markdown would also be my personal favorite for typing up your homework assignments for this course, but you are welcome to use any word processor of your choice for those. To learn more about R Markdown and for other resources for programming in R, see the links below.

  1. R for Data Science (by Hadley Wickham & Garrett Grolemund)
  2. Introduction to R Markdown (Article by Garrett Grolemund)
  3. Introduction to R Markdown (Slides by Andrew Cho)
  4. R Markdown Cheat Sheet
  5. Data Visualization with ggplot2 Cheat Sheet
  6. Other Useful Cheat Sheets
  7. A very (very!) basic R Markdown template


You may also use LaTeX to type up your assignments. You may find it easier to create your TeX and LaTeX documents using online editors such as Overleaf (simply create a free account and you are good to go!). However, that need not be the case. If you prefer to create them locally/offline on your personal computers, you will need to download a TeX distribution (the most popular choices are MiKTeX for Windows and MacTeX for macOS) plus an editor (I personally prefer TeXstudio but feel free to download any editor of your choice). Follow the links below for some options, and to also learn how to use LaTeX.

  1. Learn LaTeX in 30 minutes
  2. Choosing a LaTeX Compiler.

Interesting Articles

I will add articles I find interesting below. These are articles I find useful as supplementary readings for topics covered in class, or as good sources that cover concepts I think you should know, but which we may not have time to cover. I strongly suggest you find time to (at the very least) take a "quick peek" at each article.

  1. Efron, B., 1986. Why isn't everyone a Bayesian?. The American Statistician, 40(1), pp. 1-5.
  2. Gelman, A., 2008. Objections to Bayesian statistics. Bayesian Analysis, 3(3), pp. 445-449.
  3. Diaconis, P., 1977. Finite forms of de Finetti's theorem on exchangeability. Synthese, 36(2), pp. 271-281.
  4. Gelman, A., Meng, X. L. and Stern, H., 1996. Posterior predictive assessment of model fitness via realized discrepancies. Statistica sinica, pp. 733-760.
  5. Dunson, D. B., 2018. Statistics in the big data era: Failures of the machine. Statistics & Probability Letters, 136, pp. 4-9.