STA 360/602: Bayesian Methods and Modern Statistics

This is a rough schedule for the course and will be updated regularly. Please check this frequently for adjustments. Announcements will be posted here and made in class. It will be up to you to keep up to date on all class announcements and web announcements made for the course. Please read in ISLR or supplementary material before coming to class.

The course syllabus, labs, and course slides can be found at Course materials

Homeworks (based on lecture and lab) will all be posted on Sakai (and submissions should be done on Sakai as well). Expect about 8--10 assignments for the entire semester. (You lowest homework grade will be dropped). No late homeworks are accepted.

Supplementary reading

I have written both undergraduate and graduate level notes. Please feel free to use these to complement Hoff as needed. Please do watch out for typos!

Some of Bayesian Methods: The Essential Parts (Graduate Level), Author: Rebecca C. Steorts

Note: Chapter 5 has typos that I have no had time to fix and some parts are not as clear as I would like. Nevertheless, this should give you some extra examples and explanations different from Hoff.

Baby Bayes using R, Author: Rebecca C. Steorts

This material was meant for undergraduate students as a cross-displinary introduction to Bayesian methods, without assuming a knowledge of calculus except that a density integrates to 1. If you're having trouble with Hoff, either as an undergraduate or graduate student, consider reading parts of this. Also, there is an introduction to probability and statistics (akin with Ch 2 in Hoff). I will assume that you know this. This is all fair game for exams.

Lecture notes

  • Module 0: Introduction and Course Expectations
  • Module 1: An introduction to Bayesian methods
  • Module 2: An introduction to Decision Theory
  • Module 3: An introduction to Normal-Normal Model
  • Module 4: An introduction to Normal-Gamma Model
  • Module 5: Objective (Non-informative or Default Bayes)
  • Module 6: An introduction to Monte Carlo
  • Module 7: An introduction to Metropolis
  • Module 8: An introduction to Gibbs sampling, missing data, and data augmentation
  • Module 9: The Multivariate Normal Distribution and Missing Data
  • Module 10: Linear Regression and Probit Regression

  • Module 11: Model Selection, the g-prior, and model avergaging
    Additional readings: