This syllabus is tentative and subject to updates.

Bayesian Statistics: Calendar/Syllabus/Plan

ISyE8843: Bayesian Statistics

Fall 2004

Instructor:
Brani Vidakovic, Groseclose 320
email: brani@isye.gatech.edu,
phone: 404-894-3935
Office Hours : 12:00PM - 13:30PM Monday/Wednesday.


Class meets: 10:05 am - 10:55 am, Groseclose 304.

Textbook: Various texts in the field.

Prerequisites: No formally imposed prerequisites. Students are expected to have exposure to classical inferential statistics (at least ISyE2028 level) and a solid quantitative background (math and programming).

Grading:

  • No Homework.
  • Weekly quiz on Fridays, (the lowest score will be dropped). No makeups without a GOOD reason.
  • Exams include an in-class Midterm (Wednesday, 10/15/2004 in class) and a Final (Monday, 12/6/2004; 2:50 - 5:40). The exams will be open-book, open-notes.
  • In addition students will work on a project. Deliverable is an electronic copy of the project; also students will make presentation on their projects during last two class periods.
  • Quizzes 20%, Midterm 20%, Class Project and its Presentation 30%, Final 30%.

    Academic Honor Code: Please familiarize yourself with the Georgia Tech Honor Code.


    Week 1, 8/16-18-20

  • Introduction. Review of Probabilities, Conditional Probabilities and Bayes Formula.

    Week 2, 8/23-25-27

  • Single Parameter Models (3)

    Week 3, 8/30; 10/1-3

  • Bayesian Models (3)

    Week 4, 9/8-10 (10/6 School Holiday)

  • Bayesian Robustness

    Week 5, 9/13-15-17

  • Empirical Bayes Methods. ML II. Hierarchical Models. Hidden Mixtures.

    Week 6, 9/20-22-24

  • Model Checking and Improvement. Modeling Accounting for Data Collection.
  • Week 7, 9/27-29; 10/1

  • Bayesian Computation

    Week 8, 10/4-6-8

  • MCMC Methods. General Theory.

    Week 9, 10/11-13-15 (10/15 Midterm)

  • Basics of WinBugs.

    Week 10, 10/20-22 (10/18 Fall Break)

  • Various MCMC Topics.

    Week 11, 10/25-27-29

  • Missing Data Models.

    Week 12, 11/1-5

  • Advanced Modeling in Various Scientific Fields and Engineering. More BUGS.

    Week 13, 11/8-10-12

  • Bayesian Methods in Signal Processing and Image Analysis.

    Week 13, 11/8-10-12

  • Linear and Non-linear Regression. Bayesian Networks. (3)

    Week 14, 11/15-17-19

  • Bayesian GLM's. Categorical Data. (3)

    Week 15, 11/22-24 (11/26 School Holiday)

  • Bayesian Model and Variable Selection (2)

    Week 16, 11/29; 12/1-3

  • Bayes Discrimination (1), Student Projects (2)

    Final


    Brani Vidakovic
    8/12/2001
    Last Updated: Sunday, July 11, 2004.
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