Syllabus: Topics in Bayesian Analysis and Decision Theory.

Description: Bayesian statistics provides a wide range of tools to approach data analysis. This course is composed of (1) a Bayesian "bootcamp" facilitated by probabilistic programming languages; (2) more specialized topics, with an emphasis on computational Bayesian statistics; (3) a final project in teams or individually.

Prerequisite: STAT 460 / 560 or MATH 419 or CS 540 or equivalent (if you are not sure, come talk to me after one or two lectures).

Discussion forum: To encourage richer open exchanges, Sohrab and I will only use this platform to answer course-related questions (unless for personal matters). See Contact link in the top menu. - Environments supported: Stat net, Mac OS X, linux.

Evaluation

Exercises (they will involve coding, about 3 total) 45%
Final research project (teams encouraged) 45%
Participation: 10%
- in class and/or labs and/or piazza and/or office hours, reporting typos in notes,
- scribing/editing activity.

Handing-in files:

Name your file using your student number followed by a description of the file, e.g. 123423423-hw1.pdf. Use a zip archive if handing-in more than one file.
Go to the following URL.
Enter password bayes (no capitals)
You will see an upload button after login

Editing duties: Everyone should claim editorship of one lecture. There should be one or two editors per lecture. The editor(s) are responsible for:

Adding some supplementary references, notes, observations, etc.
Correcting errors and typos.
Participating in piazza discussion related to the lecture.
Adding the diagrams and figures that are not already included.

The editors should complete these tasks within 1 week of the lecture. This will be coordinated via github. Please create an account if you do not have one already. See below for details.

The editing process:

On the week that you will be scribing, on the day of the lecture (or before) send in a email to Sohrab your github account, he will give you access to the website github, available at this address.
The files corresponding to the lecture notes are available in _posts/. If you have not used git before, you can edit the file directly from the github website (click on a file, and then on the Edit button), but we also encourage you to learn using git, either with a graphical interface (SourceTree or Github's), or via command line (many available via google, for example there).
Please try as much as possible to edit only the file corresponding your lecture to avoid too many merge conflicts.
Note also that some changes might be pushed automatically to the website, so make sure to push commit to server only if you are sure the change is good (especially relevant if you are using the git server directly, and this is why we strongly recommend to use your own local git and then push from it).

Final project:

The course project involves independent work on a topic of your choice, with the constraint that you should make use of some of the theory covered in class, or extension of these techniques. There are three main types of projects: application, methodology, and theory, as described in class. Combinations of these is also encouraged. Extending the exercises is a good way to start thinking about project ideas.

Teams are encouraged, in which case you should outline the final report who did what.
Format: A latex document (and code if there is an empirical aspect), submitted electronically using the usual method.
Grading: I will base the grade on the same factors one would usually consider in a paper reviewing process (but taking into account the fact that the time frame is shorter than the typical time to write a research paper). Is the goal clearly defined? Is it well motivated? Is the approach sound? Creative? Is the paper well-written? Are there interesting connections made to the existing literature?

Textbook: I recommend C. Robert's "The Bayesian Choice". Other optional references will be posted at the bottom of the lecture note files.

Office hours: See Contact tab above.

Short URL for this page: http://tinyurl.com/redferret

Acknowledgement: computing supported by an AWS in Education Grant award.