Bayesian Data Analysis, Cog Sci 2011 Tutorial

Doing Bayesian data analysis with R and BUGS

Tutorial at the Conference of the Cognitive Science Society,
Wednesday, July 20, 2011, Boston, Massachusetts.

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

This tutorial introduces you to doing Bayesian data analysis, hands on. The intended audience is graduate students and other researchers who want a ground-floor introduction to Bayesian data analysis. No mathematical expertise is presumed. Complete computer programs will be provided. The software is free; see installation instructions, below, before arriving at the tutorial.

Agenda: The full-day tutorial progresses through the following topics.


Why go Bayesian? See Figure 1. But beyond that, modern Bayesian methods are the best approach to empirical data analysis because Bayesian methods yield richer inferences than traditional methods and without use of ill-defined p values. 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.


Diagram for generic Bayesian model   Diagram for generic Bayesian model
Figure 2. Concepts of Bayesian data analysis (left) transfer to Bayesian models of mind (right), but Bayesian data analysis with generic descriptive models will be useful even when specific Bayesian models of mind fail to fit real behavior.

Bayesian data analysis is not Bayesian modeling of cognition...

Bayesian data analysis uses generic descriptive models such as linear regression, without any assertions about the processes that generated the data. Bayesian methods infer credible values of parameters in the descriptive models, such as credible slopes and intercepts in linear regression, as suggested in the left side of Figure 2.

... but concepts and methods of Bayesian data analysis transfer to Bayesian models of cognition.

Because the Bayesian approach to inference is the normative approach, some cognitive scientists posit that cognitive processing itself is based on Bayesian inference by the mind, as suggested in the right side of Figure 2.

When you learn about concepts and methods of Bayesian data analysis, it is easier to understand Bayesian models of mind. But Bayesian data analysis will always be useful, even if particular Bayesian models of mind fail to accurately mimic cognition.


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. He has presented many well-received workshops on Bayesian data analysis.

His research interests include the science of morality, applications of Bayesian methods to adaptive teaching and learning, and 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. He chaired the Cognitive Science Conference in 1992.

Bringing a notebook computer?

You do not need to bring a notebook computer to the tutorial. 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 tutorial, you must install the software listed below before arriving at the tutorial, because there will not be time to do it during the tutorial and there might not be internet access.

  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 http://www.winehq.org/. For Mac users, WineBottler is reported to work for this installation. See this blog entry for more details. 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 http://cran.r-project.org/. 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. Get the 32-bit version, not the 64-bit version. Click that link and follow the installation instructions. (Even if you are using MacOS or Linux, download the 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
    install.packages("BRugs")
    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 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. Programs for the tutorial are selected from programs for the book, available here, and from more recent work, available here. (Updated July 4, 2011.)

This page URL: http://www.indiana.edu/~jkkteach/CogSci2011Tutorial.html