Department of Statistics and Biostatistics

California State University, East Bay 

Spring 2011

Stat 6550: Bayesian Statistics

Course Description Homework Important Dates Statistics Software
Syllabus Handouts   Statistics Links

Week 10:

  • The Midterm will be collected in class on Wednesday.
  • This week Bayesian GLMs.  Bayesian Logisitic Regression.
  • Prediction Limits

Week 9:

  • This week Bayesian multiple regression and stepwise regression.

Week 8:

Week 7:

  • Take-home midterm will be passed out this week.  Midterm.docx
  • Homework 2 will be collected Monday May 16.

Week 6:

  • Discussion of Bayesian Analysis of Diagnostic Testing.
  • Discussion of Metropolis-Hastings Algorithm.

Week 5:


  • Homework 2 has been posted.
  • Handouts page has been updated.

Week 4:

  • This week we will focus on introducing OpenBUGS.
  • Homework 2 will be posted this week.
  • On Monday I will answer any remaining questions about Homework 1 and it will be collected on Wed.

Week 3:

  • Here is the errata page for the book.
  • Here is the link to the website for the book.  Ioannis  The bottom of this webpage contains all of the code used in the book.
  • The Handouts page has been updated.  The Bolstad R programs have been updated and I have posted a program to do Bayesian linear regression using one of Bolstad's program.

Week 2:

  • The website has been updated to reflect that only the graduate version of the course is being offered.
  • See the Homework link for the first assignment.
  • Install the necessary software for the class. R, OpenBUGS

Week 1:

  • See the Homework link for the first assignment.
  • Please note that Blackboard will be used to post solutions to hw, quizzes, and exams, and to record grades.  All other support materials for the class will be posted here.

Week 0:

  • The book for Statistics 4870 is Introduction to Bayesian Statistics by William M Bolstad, Wiley, 2007
  • The book for Statistics 6550 is Bayesian Modeling Using WinBUGS by Ioannis Ntzoufras, Wiley, 2009.
  • Three other references for the course:
    • Bayesian Statistics an Introduction by Peter Lee, 2004.
    • Bayesian Statistical Modelling by Peter Congdon, 2001.
    • Bayesian Data Analysis, Second Edition by Gelman, Carlin, Stern, and Rubin.
    • Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians.
    • Probability Simulation and the Gibb Sampler
      • Eric Suess
      • Bruce Trumbo