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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 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.
  • Two pre-announced quizzes, each worth the same as a homework.
    • Quiz 1: 5/3/16 in class
    • Quiz 2: 6/6/16 10:30-12:20 MOR 225
  • 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.