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:


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
  1. The Basic Mathematics of Bayesian Analysis

  2. Assignment:

  3. Single Parameter Models

    Assignment:

  4. 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

  5. Bayesian Computation and MCMC Methods

  6. Assignment:

    Sixth Homework Assignment
    Homework 6 Answers (PDF)
    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

  7. Heirarchical Modeling

  8. Assignment:

  9. Regression Models

  10. Assignment:

           

Site Links

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 - 2015
Bio of Keith T. Poole
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