Bayesian Data Analysis, ICPSR 2011

Doing Bayesian Data Analysis: An Introduction.

A week-long course at the
Inter-University Consortium for Political and Social Research (ICPSR)
2011 June 20-24, University of Michigan.

Success increasing with knowledge of Bayesian data
Figure 1. Why you should attend the course.
(Notice that the Bayesian analysis reveals many credible regression lines, for which the slopes and intercepts trade off, instead of just one "best" line.)

Bayesian data analysis is rapidly replacing traditional methods because if provides richer inferences and without reference to ill-defined p values. This workshop introduces modern Bayesian data analysis, starting with ground-level concepts of probabilities and Bayes’ rule, and moving up to Bayesian hierarchical modeling applied to multiple regression and analysis of variance. Pre-requisites: Only “rusty” familiarity with integrals; no linear algebra.

Tentative agenda:

Why go Bayesian? See Figure 1. But beyond that, sciences from astronomy to zoology are changing from 20th-century null-hypothesis significance testing to Bayesian data analysis. Read more:

*Your click on this link constitutes your request to the author for a personal copy of the article exclusively for individual research.

Book cover. Who is the instructor? John Kruschke is five-time winner of Teaching Excellence Recognition Awards from Indiana University, where he is Professor of Psychological and Brain Sciences, and Adjunct Professor of Statistics. He has written an introductory textbook on Bayesian data analysis; see also the articles linked above. His research interests include models of attention in learning, which he has developed in both connectionist and Bayesian formalisms. He received the Troland Research Award from the National Academy of Sciences.

Bringing a notebook computer?

You do not need to bring a notebook computer to the course. But you are invited to bring one, so that you can run the programs and see how their output corresponds with the presentation material. If you want to bring a notebook computer to the course, you must install the software listed below before arriving at the course, because there will not be time to do it during the tutorial.

  1. One of the packages we'll be using requires the Windows operating system (OS). If your machine can operate with Windows, the tutorial materials may operate best that way. If you are using Macintosh OS or Linux without the option to boot with Windows, you must install a Windows emulator such as the freeware WINE (which stands for WINE Is Not an Emulator). WINE can be downloaded from From this point on, these instructions assume you are running Windows or a Windows (non-)emulator.

  2. Install the free programming language R. Go to In the box labelled "Download and Install R" click the "Windows" link. On the page that appears, click the "base" link. The next page that appears has the latest version of R as its top link. Click that link and follow the installation instructions. Use the 32-bit version, not the 64-bit version. (Even if you are using MacOS or Linux, download the 32-bit Windows executable and install R within WINE!)

  3. Invoke R. (If you are using MacOS or Linux, invoke the Windows version of R within WINE.) At the command line in the R console window, type
    You must include the quotes around "BRugs", and type "BR" in uppercase and everything else in lowercase. You will be prompted to select an internet repository; choose a site that is geographically near you.

    Note: You must be using a recent version of R, preferably version 2.11.0 or later, for the install.packages("BRugs") command to work properly. Are you getting an error message that the package is not available? If so, try this: In the R console window, click menu items Packages > Select Repositories, and, in the resulting pop-up window, make sure to select both CRAN and CRAN(extras), then click okay. BRugs lives in CRAN(extras). Then try install.packages("BRugs") again. Thanks to Uwe Ligges for these hints!

  4. Copy the following data analysis program to your computer and be sure that it runs. Right click this link and save the linked file on your computer, using any file name with a ".R" extension. Then, invoke R, and on the R console window click the menu items File > Source R code... Browse to your saved file and select it (and click Open or OK). The program should run in R and produce a graph in a new window and some output text in the R console.

    If the program does not run, please study all the previous steps and be sure that each was successfully accomplished.

  5. Download this zip file of data analysis programs. This file will be updated in mid June. Please check back at that time.

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