MW 3:15-4:30, 200-002

The course has an email list that reaches all TAs as well as the professor: stats306b-spr1213-staff@lists.stanford.edu.

*All questions should be directed to this email list, rather than TA or the instructor.*

- Generalized Linear Models, McCullagh & Nelder.
*Though this is marked as required, I will not follow it too closely. It is a great reference.*

I will be writing notes as we go, following in part, some of Brad Efron’s notes. Notes will include computer examples, and be written in ipython notebooks. The examples will be both in R and python.

- One parameter exponential families, part I notebook, pdf
- One parameter exponential families, part II notebook, pdf
- Multiparameter exponential families, part I notebook, pdf
- Multiparameter exponential families, part II notebook, pdf
- Multiparameter exponential families, part III notebook, pdf
- Multiparameter exponential families, part IV notebook, pdf
- Processes notebook, pdf
- Quasilikelihood notebook, pdf
- EM algorithm notebook, pdf

- Assignment 1, due Wednesday April 17, 2013. From partI, do exercises 1.2, 1.4, 1.8, 1.12, 1.13, 1.19, 1.21. From partII, do exercises 1.2, 1.4, 1.7, 1.9, 1.11.
- Assignment 2, due Monday April 29, 2013. From partI, do exercises 1.5, 1.6, 1.9, 1.10. From partII, do exercises 1.2, 1.3, 1.5, 1.6.
- Assignment 3, due Wednesday May 15, 2013. From partIII, do exercises 1.2, 1.4, 1.7. From partIV, do exercises 1.6, 1.8, 1.10, 1.14, 1.19.
- Assignment 4, due Wednesday June 12, 2013. From quasilikelihood do exercises 1.1, 1.3. From processes do exercises 1.6, 1.12, 1.17. From EM algorithm do exercises 1.1, 1.3.

I’ve created an ipython profile for the course, that will load some libraries automatically, which I will use in my examples. To use it, clone the git repo with

```
cd $HOME/.ipython
git clone https://github.com/jonathan-taylor/profile_stats306b.git profile_stats306b
```

Then, starting the notebook server with

```
ipython notebook --profile=stats306b
```

will give you access to the same profile used in executing the code.

Some familiarity with linear algebra and statistical methods, preferably having taken some of STATS300 sequence.

This is a course on exponential families and generalized linear models. We will cover the following topics (with some subject to change as we go)

- One parameter exponential families
- Multiparameter exponential families
- Generalized linear models
- Curved exponential families
- EM algorithm
- Survival analysis (?)
- Additional topics (?)

- homework (about 5 total); 50%
- final exam (according to Stanford calendar: M 6/10 @ 8:30AM); 50%