## 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. Read along in Hoff 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!
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.
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 2 Slides
- Read Read Ch 2.1 -- 2.4 of "Some of Bayesian Methods". This is not covered in Hoff.

Module 3: An introduction to Normal-Normal Model
- Module 3 Slides,
- Read Ch 2, Example 2.7 and 2.8 (in terms of variance derivations) of "Some of Bayesian Methods"

Module 4: An introduction to Normal-Gamma Model

Module 5: Objective (Non-informative or Default Bayes)
- Module 5 Slides,
- Read Hoff, Chapter 4.

- Read Chapter 5.1, 5.3 of Some of Bayesian Statistics

Remark: The slides will cover examples not always in Hoff or the notes.

Module 6: An introduction to Monte Carlo
- Module 6 Slides,
- Read Hoff, Chapter 4.

- Read Chapter 5.1, 5.3 of Some of Bayesian Statistics

Remark: The slides will cover examples not always in Hoff or the notes.

Module 7: An introduction to Metropolis
- Module 7 Slides -- Metropolis
- The reading below covers the reading for Metroplis and Gibbs sampling.

- Read Hoff, Ch 6

- Read Chapter 5.2 of "Some of Bayesian Statistics"

- For the Metropolis Algorithm, read Hoff 10.2

Module 8: An introduction to Gibbs

Module 9: Gibbs Sampling and Data Augmentation
- Module 9 Slides -- Gibbs Sampling and Data Augmentation,
- The material in class is not in the "Some of Bayesian Statistics" for the most part.

- Gibbs reading: You should have already read Ch 6 (Hoff), so review as need be.

- Metropolis Hastings: 10.4 and 10.5 (Hoff)

- Latent variable allocation: Chapter 12 (Hoff)

Module 10: The Multivariate Normal Distribution

Module 11: The Multinomial-Dirichlet Distribution

Module 12: Linear Regression

Additional readings:
- Credible Intervals): Cred intervals are covered on pages 52 and 267 of Hoff.
Read Ch 4.1--4.1 (Cred intervals) in "Some of Bayesian Statistics"

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