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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)

  • 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

      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

    6. Heirarchical Modeling

      Assignment:

      • Bayesian Computation with R, pp. 153 - 204
      • Bayesian Data Analysis, pp. 117 - 196

    7. Regression Models

      Assignment:

      • Bayesian Computation with R, pp. 205 - 264
      • Bayesian Data Analysis, pp. 353 - 442