POLI 272: BAYESIAN METHODS
Morris H. DeGroot
Born: 8 June 1931
Died: 2 November 1989
Fall Quarter AY2009-2010
Department of Political Science
University of California, San Diego
La Jolla, CA 92093-0521
Classroom: SSB 104
Time: 3:00PM - 5:50PM Thursday
Instructor:
Keith T. Poole
Office: SSB 368
E-Mail: kpoole@ucsd.edu
WebSite: Voteview Home Page or
UCSD Voteview Home Page
The following texts will be used in this course:
- Gelman, Andrew, John B. Carlin, Hal S. Stern, and Donald B. Rubin. 2004.
Bayesian Data Analysis (2nd Edition),
New York: Chapman & Hall/CRC.
- Albert, Jim. 2009. Bayesian Computation With R (2nd Edition). New York: Springer.
Requirements
This course is intended as an introduction to modern Bayesian estimation. A working knowledge
of the open-source statistical package R, OLS multiple
regression analysis, and STATA is required for this course.
Students will also be required
to learn Epsilon (EMACS), a screen editor.
We will also use the open-source Bayesian
statistical package WINBUGS along with
a variety of "canned"
programs that perform various kinds of Bayesian/Optimization analyses.
Grades will be determined by regularly
assigned class problems.
Useful Links -- WINBUGS
WINBUGS Manual (pdf file)
WINBUGS Manual With Page Numbers!! (pdf file)
Simon Jackman's WINBUGS Examples
Useful Links -- EPSILON
EPSILON HomePage -- Lugaru Software Ltd.
Useful Epsilon Commands and Examples
Useful Links --
R
An Introduction to R.
(Reference Work by R Development Core Team)
Using R for Data Analysis and Graphics: An
Introduction.
(Reference Work by J. H. Maindonald on R Graphics)
Probability Distributions in R
PCH Symbols in R
Octal References for Math Symbols that can be used in PlotMath in R
Course Outline
- The Basic Mathematics of Bayesian Analysis
Assignment:
- Single Parameter Models
Assignment:
- Multiparameter Models
Assignment:
Chap_4_Figure_4_1.r
-- R Program that produces Figure 4.1 on page 65 of
Bayesian Computation with R
Chap_4_Figure_4_2.r
-- R Program that produces Figure 4.2 on page 67 of
Bayesian Computation with R
Chap_4_Figure_4_3.r
-- R Program that produces Figure 4.3 on page 69 of
Bayesian Computation with R
Chap_4_Figure_4_4-8.r
-- R Program that produces Figures 4.5 to 4.8 on pages 69 - 76 of
Bayesian Computation with R
Chap_4_Figure_4_9-10.r
-- R Program that produces Figures 4.9 and 4.10 on pages 75 - 79 of
Bayesian Computation with R
- Bayesian Computation and MCMC Methods
Assignment:
Sixth Homework Assignment
Seventh Homework Assignment
Eighth Homework Assignment
Chap_5_Figure_5_1-2.r
-- R Program that produces Figures 5.1 and 5.2 on pages 89 - 93 of
Bayesian Computation with R
Chap_5_Figure_5_3.r
-- R Program that produces Figures 5.1 and 5.2 on pages 94 - 96 of
Bayesian Computation with R
Chap_5_MC_Integrals.r
-- R Program that does the forecasting of the heavy sleepers on page 97 of
Bayesian Computation with R
- Heirarchical Modeling
Assignment:
- Bayesian Computation with R, pp. 153 - 204
- Bayesian Data Analysis, pp. 117 - 196
- Regression Models
Assignment:
- Bayesian Computation with R, pp. 205 - 264
- Bayesian Data Analysis, pp. 353 - 442
VOTEVIEW Blog
NOMINATE Data, Roll Call Data, and Software
Course Web Pages: University of Georgia (2010 - )
Course Web Pages: UC San Diego (2004 - 2010)
University of San Diego Law School (2005)
Course Web Pages: University of Houston (2000 - 2005)
Course Web Pages: Carnegie-Mellon University (1997 - 2000)
Analyzing Spatial Models of Choice and Judgment with R
Spatial Models of Parliamentary Voting
Recent Working Papers
Analyses of Recent Politics
About This Website
K7MOA Log Books: 1960 - 2017
Bio of Keith T. Poole
Related Links