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.
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.
Module 0: An introduction R
Find a review of R and a template of what all submissions
should look like for homeworks.
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.
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