supplementary material
code
data
labs


CSSS-Stat 564

Bayesian Statistics



Lectures: TTh, 10:30-11:50 , MOR 225
Lab: Th, 1:30-2:20, SMI 311
Instructor
  • Peter Hoff ( pdhoff )
  • C-319 Padelford
  • Office Hours: 10:30-11:30 M and W
Teaching Assistant
  • Maryclare Griffin ( mgrffn )
  • C-318 Padelford
  • Office Hours: 11:30-12:30 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, 7.1-7.4
    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

  1. Concepts of randomness and probability
  2. Review of probability calculus
  3. Inference for binomial, Poisson and normal distributions
  4. Hierarchical models
  5. Multivariate normal distribution
  6. Linear regression models
  7. Generalized linear models
  8. Generalized linear mixed-effects models
Additionally, we will cover the basics of Monte-Carlo integration and Markov chain Monte Carlo (Gibbs sampling and the Metropolis-Hastings algorithm). This material will be covered concurrently with the material listed above.
Evaluation
  • Eight or so homework assignments.
  • A pre-announced quiz, worth the same as a homework.
    • Quiz 1: 5/3/16 in class
  • 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.