STA 360/601: 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. Read along in Hoff before coming to class.

Slides and notes for this class are based upon many different references and notes that I have written.

The course syllabus can be found here Syllabus: things you need to know about the course!

You lab schedule and homeworks will all be posted on Sakai (and submissions should be done on Sakai as well). Expect one homework per week. Yes, we have lab the first week of class.

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: An introduction R
  • Find a review of R and a template of what all submissions should look like for homeworks.
  • Module 1: An introduction to Bayesian methods
  • Module 1: An introduction to Bayesian methods, part II
  • Module 2: An introduction to Decision Theory
  • Module 3: Advanced Bayes
  • Module 4: Objective ("Default Bayes")
  • Coverpage for Exam One
  • Module 5: Monte Carlo
  • Module 6: Introduction to Markov Chain Monte Carlo
  • Module 7: Introduction to Gibbs Sampling: Two Stage Gibbs Sampling
  • Module 8: Introduction to Gibbs Sampling: Multi-stage Gibbs Sampling, Missing Data, and Latent Variable Allocation
  • Module 9: Metropolis Hastings
  • Module 10: Multivariate Methods
  • Module 11: An Intro to Bayesian Nonparametrics
  • Last Lecture: Exam Format and Course Evaluations