Bayesian Data Analysis, Purdue 2011

## Workshop: Introduction to Doing Bayesian Data Analysis

### Purdue University Saturday, Oct. 22, 2011, 9:00am - 12:00noon Stewart Center (STEW), room 214

 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 workshop shows you how to do Bayesian data analysis, hands on (with free software called R and BUGS). The intended audience is grad students, faculty, and other researchers who want a ground-floor introduction to Bayesian data analysis. No mathematical expertise is presumed.

Agenda:

• The perils of p values. (More reasons to go Bayesian are linked below. See also Figure 1.)
• Bayes' rule, grid approximation, and R.
• Markov chain Monte Carlo and BUGS. This does not involve any physical restraints or insects. Unfortunately, it also does not involve Monte Carlo.
• Coffee break! Everything tastes better when it's Bayesian.
• Bayesian comparison of means and Bayesian ANOVA with multiple comparisons. Bayesian comparison of means avoids fatal flaws in null hypothesis significance testing.

Strongly recommended textbook: Doing Bayesian Data Analysis: A Tutorial with R and BUGS. See some published reviews of the book here. Other endorsements and information about the book can be read here.

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. The concepts and methods of Bayesian data analysis transfer to other Bayesian models, including Bayesian models of cognition.

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:

• An open letter explaining why it's time to go Bayesian.
• An article* that explains a critical flaw of p-values in null hypothesis significance testing, and two different Bayesian approaches to assessing null values.
Kruschke, J. K. (2010). Bayesian data analysis. Wiley Interdisciplinary Reviews: Cognitive Science, 1(5), 658-676. (doi:10.1002/wcs.72)
• An article* that emphasizes advantages of Bayesian data analysis and the fact that Bayesian data analysis is appropriate regardless of the status of Bayesian models of cognition.
Kruschke, J. K. (2010). What to believe: Bayesian methods for data analysis. Trends in Cognitive Sciences, 14(7), 293-300. (doi:10.1016/j.tics.2010.05.001)
• An article* that explains two Bayesian methods to assess null values, and which one is typically more informative.
Kruschke, J. K. (2011). Bayesian assessment of null values via parameter estimation and model comparison. Perspectives on Psychological Science, 6(3), 299-312.
*Your click on this link constitutes your request to the author for a personal copy of the article exclusively for individual research.

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 the science of moral judgment, applications of Bayesian methods to 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 is action editor for the Journal of Mathematical Psychology, and is on the editorial boards of Psychological Review, the Journal of Experimental Psychology: General, among others.