Welcome to STA 360-602!

The readings for the class can be found below. These are subject to change and will be updated as the course progresses.

The readings are from Hoff and also in notes that I have written myself. You'll notice that some of the material covered in class are from these notes. I expect you to have read before you come to class and have reviewed the notes from the previous lectures. I highly recommend that you read _all of Hoff. It's a great book to read.

Module 0 (Intro to R programming): This will be a review for most students. If you would like a reference text, I recommend http://shop.oreilly.com/product/9780596809164.do (The R Cookbook, not to be confused with the one for graphics).

Module 1 (Why Bayes) : Read Ch 1, Ch 2.1 -- 2.6. (Hoff) Read Ch 1.1 (http://www2.stat.duke.edu/~rcs46/books/bayes_manuscripts.pdf)

Module 1 (Continued) (Intro to Bayes) : Read Ch 3 (Hoff) Read Ch 2.5--2.7 (http://www2.stat.duke.edu/~rcs46/books/bayes_manuscripts.pdf) Read Ch 4 for predictive inference (Hoff). Read Ch 2.9 (Posterior predictive inference) (http://www2.stat.duke.edu/~rcs46/books/bayes_manuscripts.pdf).

Module 2 (Decision Theory): Read Ch 2.1 -- 2.4 (http://www2.stat.duke.edu/~rcs46/books/bayes_manuscripts.pdf) This is not covered in Hoff.

Module 3 (Advanced Conjugacy, Normal-Normal Model): Ch 2, Example 2.7 and 2.8 (in terms of variance derivations), (http://www2.stat.duke.edu/~rcs46/books/bayes_manuscripts.pdf)

Module 4 (Advanced Conjugacy, Normal-Gamma): Ch 2, Example 2.13, (http://www2.stat.duke.edu/~rcs46/books/bayes_manuscripts.pdf)

Module 5 (Importance Sampling and Rejection Sampling): Read Hoff, Chapter 4. Read PhD notes, Chapter 5.1, 5.3 Remark: The slides will cover examples not always in Hoff or the notes.

Modules 6--7 (One Stage Gibbs Sampling and the Metropolis Algorithm) Read Hoff, Ch 6 Read Phd notes, Chapter 5.2 Remark: The slides will cover examples not always in Hoff or the notes.
For Metropolis Algorithm, read Hoff 10.2

Module 8 (Multistage Gibbs and Latent Variable Allocation) The material in class is not in the PhD notes for the most part. (Note: I will not be following the book with regards to the examples in Hoff but I do recommend reading them). Gibbs reading: You should have already read Ch 6, so review as need be. Metropolis Hastings: 10.4 and 10.5 Latent variable allocation: Chapter 12

Module 9 (Multivariate Normal Distribution) Hoff: Chapter 7.1--7.4

Module 10 (The Multinomial Dirichlet Distribution) This is not based upon any notes or reading. This is just an example of illustrating the Dirichlet is conjugate to the Multinomial distribution.

Here is a brief intro from PSU on Multinomial sampling for a review: https://onlinecourses.science.psu.edu/stat504/node/59

Module 11 (Linear Regression) Hoff: Chapter 9.1--9.2

Other topics: Credible Intervals): Cred intervals are covered on pages 52 and 267 of Hoff. Read Ch 4.1--4.1 (Cred intervals) (http://www2.stat.duke.edu/~rcs46/books/bayes_manuscripts.pdf)