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:
- Basics of Bayesian analysis, BDA chapters 1:5 and parts of 6:9
- Bayesian computation, BDA chapters 10:13 and Appendix C
- 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 stanfordPelican.edu
(remember to remove the waterfowl)
- Office: Tuesday 11 to 12
||Sequoia 207 (Bowker)
||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.
Be sure to give Axess a working email address:
The problem sets are available to students registered
in the class. The existence of a new problem set will be announced in class.
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.)
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.