Below are source (Rnw) and handouts for all course lectures. Instructions for compiling the Rnw files can be found below.

Chapter slide source files

Chapter Topic Source Handouts
1 Bayesian statistics Ch01.Rnw Ch01.pdf
2 Binomial model/priors Ch02a.Rnw Ch02a.pdf
  Normal model/computation Ch02b.Rnw Ch02b.pdf
3 Independent binomials/normal Ch03a.Rnw Ch03a.pdf
  Multinomial/multivariate normal Ch03b.Rnw Ch03b.pdf
4 Data asympotitics Ch04.Rnw Ch04.pdf
5 Binomial hierarchical model Ch05a.Rnw Ch05a.pdf
  de Finetti/normal hierarchical model Ch05b.Rnw Ch05b.pdf
6 Model checking Ch06.Rnw Ch06.pdf
7 Hypothesis testing Ch07a.Rnw Ch07a.pdf
  Comparison to LRTs Ch07b.Rnw Ch07b.pdf
9 Decision theory Ch09.Rnw Ch09.pdf
R Amazon reviews analysis AmazonReview.Rnw AmazonReview.pdf
  Midterm review MidtermReview.Rnw MidtermReview.pdf
  Bayesian model averaging BMA.Rnw BMA.pdf
10 Bayesian computation Ch10a.Rnw Ch10a.pdf
  ARS and Importance sampling Ch10b.Rnw Ch10a.pdf
11 Metropolis-Hastings Ch11a.Rnw Ch11a.pdf
  Gibbs sampling Ch11b.Rnw Ch11b.pdf
  Markov chains Ch11c.Rnw Ch11c.pdf
  Markov chain Monte Carlo Ch11d.Rnw Ch11d.pdf
14 Bayesian regression Ch14a.Rnw Ch14a.pdf
  Bayesian regression (cont.) Ch14b.Rnw Ch14a.pdf
15 Hierarchical linear models Ch15a.Rnw Ch15a.pdf
  Hierarchical linear models (cont.) Ch15b.Rnw Ch15b.pdf
16 Hierarchical linear models Ch16a.Rnw Ch16a.pdf

Below are to be organized:

Rnw compilation instructions

The following files contain code to create slides for the associated chapters of Bayesian Data Analysis, 3rd edition. You will need to have the R package knitr installed, i.e.


To obtain the pdf, you will need to have LaTeX installed and in the path (etc). Then download the Rnw file (as an example, I will use Ch01.Rnw) and run


Alternatively, you can install RStudio and click on the Compile PDF button.

To extract the R code, run

knitr::knit('Ch01.Rnw', tangle=TRUE)