.. stats306b documentation master file, based on matplotlib sampledoc Syllabus ======== ******************* Schedule & Location ******************* MW 3:15-4:30, 200-002 **************** Instructor & TAs **************** Instructor ---------- `Jonathan Taylor `_ * Office: Sequoia Hall #137 * Phone: 723-9230, * `Email `_ * Office hours: F 12:00-2:00 Email list ---------- 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.* Teaching assistants ------------------- * Yunjin Choi * Office: Sequoia Hall, #208 * `Email `_ * Office hours: M 9:00-11:00 * Alexandra Chouldechova * Office: Sequoia Hall, #242 * `Email `_ * Office hours: W 11:00-1:00 ******** Textbook ******** * `Generalized Linear Models `_, McCullagh & Nelder. *Though this is marked as required, I will not follow it too closely. It is a great reference.* ***** Notes ***** 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 `_ *********** Assignments *********** * 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. Ipython profile --------------- 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 .. code-block:: bash cd $HOME/.ipython git clone https://github.com/jonathan-taylor/profile_stats306b.git profile_stats306b Then, starting the notebook server with .. code-block:: bash ipython notebook --profile=stats306b will give you access to the same profile used in executing the code. ************* Prerequisites ************* Some familiarity with linear algebra and statistical methods, preferably having taken some of STATS300 sequence. ************** Topics covered ************** 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 (?) ********** Evaluation ********** * homework (about 5 total); 50% * final exam (according to Stanford calendar: M 6/10 @ 8:30AM); 50% Final exam ---------- * Following the Stanford `calendar `_: Monday, June 10 @ 12:15PM. * If you cannot take the exam at that time and day, then you will have to take this class in a different quarter. Exceptions will only be made due to official university affairs, such as athletic commitments. *********** R resources *********** * `An Introduction to R `_ * `R for Beginners `_ * `Using R for Introductory Statistics `_ * `Modern Applied Statistics with S `_ * `Practical ANOVA and Regression in R `_ * `simpleR `_ * `Introduction to R `_ * `R Reference Card `_ * `R Manuals `_ * `R Wiki `_ **************** python resources **************** * `IPython `_ * `Numpy and scipy `_ * `Numpy tutorial `_ * `Python tutorial `_