Department of Statistics

California State University, East Bay 

Summer 2007

Stat 4870/6870: Introduction to Bayesian Statistical Analysis

Course Description Homework Important Dates Statistics Software
Syllabus Handouts Stat Lab Times Open Statistics Links
Blackboard    

Week 10:

  • The webpage for the Gibbs Sampling and Screening Tests STATS paper.  Gibbs
  • Has everyone tried out BetaBuster or the following webpages from Epi Tools, BetaParams1 BetaParams2.
  • DIC: Deviance Information Criteria  Consider the pumps2.odc
  • Some example handouts of R2WinBUGS and BRugs have been posted.
  • This week we will consider the One-way Random Effects ANOVA model.  The handouts from the 2005 JSM poster presentation are available at onewayrand
  • If you have time there is a Homework 5 that ask you to run the programs related to the examples presented in class.
  • JSTORE - online statistics and probability articles, available on campus.
  • Understanding the Metropolis-Hastings Algorithm
    Siddhartha Chib, Edward Greenberg
    The American Statistician, Vol. 49, No. 4 (Nov., 1995), pp. 327-335
  • Final Exam is on Wed. Sept. 5

 

Review for the Final.  Read the following

Week 9:

Week 8:

  • Project: The project for the class will be to find a topic of interest to you and to find an example WinBUGS program to understand, run, and interpret the results from.  To start you might look over the examples in WinBUGS. Next week on Wednesday a proposal for your project will be collected which should include
    1. The topic of interest.  That is the area of application of you project. 
    2. Give a description of the statistical methods used in the example program you have decided to work on. 
    3. Describe the data and the parameters in the model. 
    4. Describe what the parameters if interest are in the model and what the possible inferences will be.
  • Two-sample problems Lee  WinBUGSprograms: sleep.txt  ratdiet.txt  ratdiet_unknownvar.txt
  • Homework 4 has been posted.
  • Take a look at this website for information about Diagnostic Testing. BEST  And try BetaBuster for beta prior selection.

Week 7:

Week 6:

  • Monday's class will be cancelled because I will be at the JSM meetings in Salt Lake City.
  • The previously scheduled presentation by Joseph Rickert will be postponed until the week of August 13. 
  • The take-home quiz will be due on Wednesday.
  • Continue to work on the new homework.  We will discuss the hw in class on Wednesday.
  • There will be a Midterm review on Wednesday for the on the following Monday August 6.

Week 5:

  • Homework 3 has been posted.
  • The presentation slides from the History of Probability and Are you a Bayesian? have been posted on Blackboard.  Note that everyone is in the 4870 Blackboard site.
  • Next week on Monday I will be gone to a conference. There will be another presentation by Joseph Rickert, An introduction to weka and its use of Bayesian Statistics.  (Postponed until the week of August 13.)
  • Take-home Quiz 1  quiz1takehome.pdf

Week 4:

  • Homework 2 will be due next week and Homework 3 will be posted this week.
  • Quiz 1 will be next week on Monday July 23.
  • The Midterm will be on Monday August 6.

Week 3:

  • Homework 1 will be collected on Wednesday July 11.
  • Homework 2 has been posted.

Week 2:

  • Because of the 4th of July Holiday we will not have class on Wednesday.
  • Today we will go to the computer lab during the second hour of the class to introduce R and WinBUGS.
  • This week the 4870 students (and anyone interested) should install the R library Bolstad.

Week 1:

  • See the Homework link for the first assignment.
  • Install the necessary software for the class. R, WinBUGS, OpenBUGS

Week 0:

  • The book for Statistics 4870 is Introduction to Bayesian Statistics by William M Bolstad, Wiley, 2004
  • Bolstad
  • The book for Statistics 6870 is Bayesian Statistics and introduction by Peter Lee, Hodder Arnold, 2004.
  • Lee     Rprograms     WinBUGSprograms
  • Two other references for the course:
    • Bayesian Statistical Modelling by Peter Congdon, 2001.
    • Bayesian Data Analysis, Second Edition, by Gelman, Carlin, Stern, and Rubin.