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CSSSStat 564
Bayesian Statistics
Lectures:
TTh, 10:3011:50 , MOR 225
Lab:
Th, 1:302:20, SMI 311
Instructor
 Peter Hoff ( pdhoff )
 C319 Padelford
 Office Hours: 10:3011:30 M and W
Teaching Assistant
 Maryclare Griffin ( mgrffn )
 C318 Padelford
 Office Hours: 11:3012:30 W and F
Please include "564" (without quotes) in any emails to allow for appropriate filtering.
Texts
Schedule
Week 9:
 Reading: Finish PH Chapter 8, start Chapter 9.
 Homework: Homework 7 due Tue 5/31/16.
Week 8:
 Reading: Finish PH Chapter 7, start Chapter 8.
 Homework: 7.4 and 7.6. For 7.4 part d, only do d.iii. Due Tue
5/24/16.
Week 7:
 Reading: Finish PH Chapter 6, 7.17.4
Week 6:
 Reading: PH 5.3, 5.4, 6.1.
 Homework: Homework 5 due Tue 5/17/16.
Week 5:
 Reading: PH Chapter 5.
 Homework: Book exercises 5.1 and 5.2, due Thu 5/5/16.
Week 4:
 Reading: PH Chapter 4 and start Chapter 5.
 Homework: Book exercises 4.2 and 4.3, due Tue 4/26/16.
Week 3:
 Reading: PH Chapter 3 and start Chapter 4.
 Homework: Book exercises 3.2, 3.3 and 3.9, due Tue 4/19/16.
Week 2:
Week 1:
 Reading: PH Chapter 1. Start Chapter 2.
 Homework: Homework 1 is due Tue 4/12/16.
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.
Evaluation
 Eight or so homework assignments.
 A preannounced quiz, worth the same as a homework.
 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.
 I follow the UW grading system for graduate students. The distribution of grades I gave the last time
I taught this course is here.
