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.

- Module 0: Course Expectations,
- Module 0: An Intro to R (Go over on your own)
- Lab 0: An Intro to R
- If you would like a reference text for R, I recommend The R Cookbook
- To review linear algebra, I recommend MIT Linear Algebra Videos

- Intro to Bayes, Part I ,
- Intro to Bayes, Part II ,
- Read Ch 1, Ch 2.1 -- 2.6. (Hoff)

Read Ch 1.1, 2.5--2.7, 2.9 of "Some of Bayesian Methods"

Read Ch 4 for predictive inference (Hoff).

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

- Module 3 Slides,
- Read Ch 2, Example 2.7 and 2.8 (in terms of variance derivations) of "Some of Bayesian Methods"

- Module 4 Slides,
- Read Ch 2, Example 2.13 of "Some of Bayesian Methods"

- 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 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 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 Slides -- Gibbs Sampling,
- Module 8 Slides (Part II) -- Gibbs Sampling and Missing Data,
- Module 8 Slides (Part III) -- Gibbs Sampling and Data Augmentation,
- Module 8 Slides (Part IV) -- Data Augmentation, The Dirichlet Multinomial, and Mixture Models,
- 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 9 Slides -- The Multivariate Normal Distribution,
- Module 9 Slides -- The Multivariate Normal Distribution and Missing Data,
- Read Hoff: Chapter 7.1--7.4

- Module 10 Slides -- Linear Regression,
- Module 10 Slides -- Probit Regression,
- Read Hoff: Chapter 9

- Module 11 Slides -- Model Selection,
- Read Hoff: Chapter 12

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"

Top