Bayesian Data Analysis, SCiP 2010

Doing Bayesian data analysis with R and BUGS

Tutorial at the Society for Computers in Psychology (SCiP) conference, November 18 2010, the day before the conference of the Psychonomic Society, at which there will be a Symposium on practical benefits of Bayesian data analysis

UPDATED 3:45pm EST, NOVEMBER 2, 2010

 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 half-day tutorial shows you how to do Bayesian data analysis, hands on. The software is free; see installation instructions, below, before arriving at the tutorial. The intended audience is grad students and other researchers who want a ground-floor introduction to Bayesian data analysis. No mathematical expertise is presumed. If you can handle a few minutes of summation notation like Σixi and integral notation like ∫ x dx, you're good to go. Complete computer programs will be worked through, step by step.

Agenda. All tutorial sessions below are convened in the Soulard room of the Millennium Hotel, St. Louis, on the morning of November 18, 2010.
8:00-9:00 Bayes' rule, grid approximation, and R. (See installation instructions, below, before arriving at the tutorial.)
• 9:00-9:15 Break.
9:15-10:15 Markov chain Monte Carlo and BUGS.
• 10:15-10:30 Break.
10:30-11:30 Linear regression.
• 11:30-11:45 Break.
11:45-12:45 Hierarchical models.

Bayesian data analysis is not Bayesian modeling of cognition. Data analysis involves "generic" descriptive models (such as linear regression) without any necessary interpretation as cognitive computation. The rational way to estimate parameters in descriptive models is Bayesian, regardless of whether or not Bayesian models of mind are viable.

Why go Bayesian? See Figure 1. For a more serious, yet brief discussion of several benefits of Bayesian data analysis, along with an example and an emphasis that Bayesian data analysis is not Bayesian modeling of mind, see this article from Trends in Cognitive Sciences. For a lengthier exposition that explains a fatal flaw of 20th century null hypothesis significance testing, along with a discussion of Bayesian null hypothesis testing and other examples, see this article from Wiley Interdisciplinary Reviews: Cognitive Science.

Who is the instructor? John Kruschke has taught introductory Bayesian statistics to graduate students for several years (and traditional statistics and mathematical modeling for over 20 years). He 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 a Troland Research Award from the National Academy of Sciences. He chaired the Cognitive Science Conference in 1992.