Stat 305C: Applied Bayesian Statistics


We are going to do applied Bayesian statistics, using the Gelman et al BDA 3 book. This is the first time this class has been offered at Stanford.
There will be about 6 problem sets. Students are expected to use R to do the problem sets.
You should know linear and generalized linear modeling already (theory and practice), as well as the usual statistical distributions and basic (pre measure theory) probability.


We will work in three main modules:
  1. Basics of Bayesian analysis, BDA chapters 1:5 and parts of 6:9
  2. Bayesian computation, BDA chapters 10:13 and Appendix C
  3. Specific analyses:
    • Regression and hierarchical models, BDA chapters 14:16
    • Spike and slab (not in BDA)
    • Bayesian empirical likelhood (not yet in BDA)
    • Bayesian nonparametrics, BDA chapter 23


Mitchell Building, B67
Monday, Wednesday 3:00 to 4:20


Art Owen
Sequoia Hall 130
My userid is owenPelican on (remember to remove the waterfowl)
Office: Tuesday 11 to 12


Day Time TA Office email Meeting room
Thursday 3-5pm Rina Friedberg 241 Sequoia Library
Monday 11am-1pm Lucas Janson 231 Sequoia 207 (Bowker)
Friday 12-2pm Keli Liu 237 Sequoia 207 (Bowker)

Delete any and all Antarctic birds from the TA's email


The text is Bayesian Data Analysis, Third Edition by Gelman, Carlin, Stern, Dunson, Vehtari and Rubin (2014).


Here is a problem set guide for students taking this course. Here is a guide for TAs grading this course.

The problem sets are available to students registered in the class. The existence of a new problem set will be announced in class.
Be sure to give Axess a working email address:
I expect to send a small number of important emails about problem sets and the homework there. Most other announcements will be made in class. If you email me about the class, be sure to have stat 305 in your subject line. Otherwise, your email won't show when I search for course related emails.
Late penalties apply:
We will count days late on each problem set. Each day late is penalized by 10% of the homework value. Homework more than 3 days late will ordinarily get 0. If you're travelling, you can email a pdf file. For sickness, interviews and other events, up to 3 late days total are forgiven at the end of the quarter. (Work late enough to get zero does not get redeemed though.)

Supplementary materials

Here are my Monte Carlo notes. Chapter 7 has a short section on the Laplace approximation. Chapter 9 is a lengthy chapter on importance sampling.
Here is Andrew Gelman's blog. He writes often about getting the right answer from statistics. Sometimes he says things I've thought but never seen in writing before. Sometimes I see completely new insights. He has lots of good specific examples of data analysis gone wrong with careful point by point critiques. Good statistical practice is not just about the math or the computing but about how they interact with the underlying science and goals.

xkcd on correlation versus causation. This is the first funny statistics joke I have ever seen. Lawyers have so many more to choose from.