36-724: Applied Bayesian and Computational Methods

Time/Place MWF 2:30-3:30, Porter Hall 125B
Website: http://www.stat.cmu.edu/~acthomas/724/.
Instructor: Andrew C. Thomas (acthomas -at- stat.cmu.edu). Office Hours: Thursday, 3:00-5:00, Baker Hall 132H (or by appointment).
TA: Dancsi Percival (dperciva -at- stat.cmu.edu). Office Hours: Friday 10:00-11:00, Wean 8110.

Remaining Class Schedule: Class as usual February 20, 22, 24. No class February 27. Special presentations by Prof. Junker on February 29, Prof. Shalizi on March 2. One final class meeting March 5.

Required text:

  • Gelman, Carlin, Stern and Rubin (2003) Bayesian Data Analysis, Second Edition. Chapman & Hall. I will often refer to this in shorthand as "the red book".

    Suggested texts:

    Prerequisites: 36-705 ``Intermediate Statistics'' and 36-707 ``Intermediate Regression''. Or, permission of the instructor.

    Outline: The goal of this course is to give a meaningful introduction and exploration of Bayesian statistical methods through computational techniques in a short course. We will focus on the principles of Bayesian hierarchical modelling methods that can be programmed efficiently and remain scientifically valid, and methods for debugging without pulling too much hair out. We will not be explicitly covering discriminative machine-learning topics, but we will cover the same debugging concepts that will make things easier when coding them up.

    Grading: There will be six weekly homework assignments. The lowest score will be dropped, so that each of your top homeworks are worth 16% of the total, plus 20% for homework follow-ups, classroom discussion and participation. No late homeworks will be accepted! There will be no final exam.

    Programming language: R will be the only supported language for the course.

    Course Notes: Available here; will be continually updated throughout the course.

    Demo files:

    Assignments: Submit to 724homeworksgohere@gmail.com. Email submissions only!

    Reading:

    Tentative outline of the course (subject to reordering, but topics will likely be conserved):

    References: