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CSSSStat 564
Bayesian Statistical Methods
Lectures
MWF, 9:3010:20, EEB 054
Lab
Th, 12:301:20, Communications B027
Instructor
 Peter Hoff
 C319 Padelford
 Office Hours: Tue 9:3010:30, Wed 10:3011:30 or by appointment.
 pdhoff at uw dot edu
Teaching Assistant
 Alex Volfovsky
 B228 Padelford
 Office Hour: TBA
 volf at uw dot edu
Please include "564" (without quotes) in any emails to allow for appropriate filtering.
Texts
Assignments
 Week 10: Read Chapter 10 and do the final homework.
 Week 9: Read Chapter 9 and do exercises 9.2(a) and all of 9.3, to be turned in Friday June 3.
 Week 8: Read Chapter 8 and do exercises 8.1 and 8.3, to be turned in
Friday May 27.
 Week 7: Read Chapter 7 and do exercises 7.4 and 7.6. For 7.4 part d, only do d.iii to be turned in Friday May 20.
 Week 6: Read Chapter 6 and do these exercises to be turned in Friday May 13.
 Week 5: Read Chapter 5 and do exercises 4.8 and 5.1 and 5.2 to be turned in Friday May 6.
 Week 4: Read Chapter 4 and do exercises 3.3 part (a), 4.1 and 4.2, to be turned in Wednesday April 27. For 4.2 (b) use n0 values between 1 and 10. For 4.2 (c2) you only have to repeat part 4.2 (a) using the predictive distribution.
 Week 3: Read Chapter 3 and and do exercises 3.2, 3.7 and 3.9, to be turned in Wednesday April 20.
 Week 2:
 Week 1:
Read Chapter 1 and start Chapter 2 of the text.
Evaluation
 Eight or nine homework assignments
 Two or three inclass quizzes, which will be announced in advance
Each quiz will be given the same weight as a homework. There will be the opportu
nity
to correct quiz mistakes for partial credit.
Course Outline
 Concepts of randomness and probability
 Review of probability calculus
 Inference for binomial, Poisson and normal distributions
 Hierarchical models
 Multivariate normal distribution
 Linear regression models
 Generalized linear models
 Generalized linear mixedeffects models
Additionally, we will cover the basics of
MonteCarlo integration and Markov chain Monte Carlo
(Gibbs sampling and the MetropolisHastings algorithm).
This material will be covered concurrently with
the material listed above.
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 day to be applied to
one homework for the entire quarter.
