R and JAGS code seen in lectures
Instructor
 Youyi Fong
 Padelford C14E
 Office Hours: T/Th 12:301:30 or by appointment
 yfong@uw.edu

Teaching Assistant
 Wen Wei Loh
 Padelford C312
 Office Hours: Tues 4:305:30 and Wed 3:004:00
 wloh@uw.edu

Please include "564" (without quotes) in any emails to allow for appropriate filtering.
Texts/Resources
Assignments Week 10:
 Week 9:
 Read Chapter 11
 HW8, due Thu, May 31
 11.2 (a) to (d). In (b), use 5 degrees of freedom for Wishart. In (c), use JAGS to do the sampling. Remember to center every covariate. In (d), just plot the diagonal elements of Sigma.
 Extra credit 11.2 (e)
 Week 8:
 Read Chapter 9, 10
 HW 7, due Thu, May 24
 10.2 (d), (e). Use JAGS to perform posterior sampling. Try two different priors as we did for the Poisson regression example.
 default prior
 'unit information' prior
 Week 7:
 Read Chapter 8,
 HW 6, due Thu, May 17
 8.1.
 8.3, (a), (b), (c) and (e). You can use JAGS to do Gibbs sampling in (a) or write your own Gibbs code, extra credit for doing both.
 Week 6 (April 29):
 Read Chapter 6, 7
 HW5, due Thu, May 10
 6.1 (a) and (d). Also draw a graphical model. In (d), use JAGS to do the sampling.
 7.4. For part (d), only do d.iii, draw and graphical model and use JAGS to do the sampling.
 Week 5:
 Read Chapter 5 and 6
 HW4, due Thu, May 3
 5.2
 This week's homework is light because your onepage project summary is due soon on May 8. Please come talk to me if you have any questions.
 Extra credit: 5.1
 Week 4:
 Read Chapter 4 and 5
 HW3, due Thu, April 26
 3.3 part (a), 4.2. For 4.2 (b) use n0 values between 1 and 10. For 4.2 (c) you only have to repeat part 4.2 (a).
 4.8 (a)(b)
 Extra credit: 4.8 (c)(d)
 Week 3:
 Read Chapter 3 and 4
 Do exercises 3.2, 3.7 (also draw a graphical model and write a JAGS model file ); extra credit: 3.9 (a)(c). Due Thu, April 19.
 Week 2:
 Week 1: Read Chapter 1 and 2 of the text
Evaluation
 Eight homework assignments
 Final project, count as two homework assignments
Course Outline
 Introduction to Bayesian statistics
 Concepts of randomness and probability, review of probability calculus
 Inference for binomial and Poisson distributions
 Monte Carlo
 Inference for normal distribution
 Hierarchical models
 Multivariate normal distribution
 Linear regression models
 Generalized linear models
 Generalized linear mixedeffects models
Late policy
Each turned in item receives an initial grade of x, then the actual grade is y=x exp(d/8), where d is the number of days (including weekends) after the due date I receive the work. Everyone receives one grace period to be applied to one homework for the entire quarter.
Notes 4/12/12
 Final project planning
 One page summary of background, data and model due on May 8
 Meet to discuss projects in half an hour blocks during 12:303:30 on May 8 or 12:301:30 on May 10
 1520 min class presentations and a report (23 page text + up to 3 tables and/or figures) due on May 31
4/10/12

