Bayesian Data Analysis, Univ. St. Gallen 2013

## Doing Bayesian Data Analysis a four-day course at University of St. Gallen, Switzerland June 10 - 13, 2013

### Instructor: John Kruschke Offered through the University of St. Gallen Summer School in Empirical Research Methods (SSERM)

Many fields of science are transitioning from null hypothesis significance testing (NHST) to Bayesian data analysis. Bayesian analysis provides complete information about the relative credibilities of all candidate parameter values for any descriptive model of the data. Bayesian analysis applies flexibly and seamlessly to complex hierarchical models and realistic data structures, including small samples, large samples, unbalanced designs, missing data, censored data, outliers, etc. Bayesian analysis software is flexible and can be used for a wide variety of data-analytic models. The course shows you how to do Bayesian data analysis, hands on (with free software called R and JAGS). The intended audience is advanced students, faculty, and other researchers, from all disciplines, who want a ground-floor introduction to doing Bayesian data analysis. No mathematical expertise is presumed.

This course is offered through the University of St. Gallen Summer School in Empirical Research Methods. Registration information is included below.

Course Topics:
 A posterior probability distribution for parameters that describe two groups, showing complete distributions of the difference of means (right middle), the difference of standard deviations, the effect size (right bottom), and posterior predictive check (right upper).

• Bayesian reasoning.
• Bayes' rule, grid approximation, and the programming language R. Example: Estimating the bias of a coin.
• Markov Chain Monte Carlo and JAGS. Example: Estimating parameters of a normal distribution.
• HDI, ROPE, decision rules, and null values.
• Robust Bayesian estimation of difference of means.
• The NHST t test. Why to go Bayesian.
• Power: Probability of achieving the goals of research. Applied to Bayesian estimation of groups.
• Sequential testing.
• The generalized linear model.
• Simple linear regression. Exponential regression. Sinusoidal regression. With auto-regressive AR(1) trend.
• How to modify a program in JAGS & rjags for a different model.
• Robust regression for accommodating outliers, for all the models above and below.
• Multiple linear regression.
• Logistic regression.
• Ordinal regression.
• Hierarchical models: Estimates of means at individual and group levels. Shrinkage.
• Examples of hierarchical models with beta distributions: therapeutic touch, meta-analysis of ESP, baseball batting averages.
• Hierarchical regression models: Estimating regression parameters at multiple levels simultaneously.
• Hierarchical model for shrinkage or regression coefficients in multiple regression.
• Bayesian hierarchical one way ANOVA. Multiple comparisons and shrinkage.
• Bayesian ANOVA with unequal variances.
• Bayesian hierarchical two way ANOVA with interaction. Interaction contrasts.
• Split plot design.
• Log-linear models and chi-square test.
• Model comparison as hierarchical model. The Bayes factor. Doing it in JAGS.
• Two Bayesian ways to assess null values. Estimation is better than model comparison.
• How to report a Bayesian analysis.
• Misc advanced topics: Censored data in JAGS. Mixture of normals.
The content for the 2013 course is being updated and expanded.

Register with the University of St. Gallen.

This course is offered through the University of St. Gallen Summer School in Empirical Research Methods, so you must register to attend. Late registration will be allowed for a limited time. Complete registration and contact information is at this link. The instructor has no control of fees or registration procedure.

Install software before arriving.

You are encouraged to bring a notebook computer to the workshop, so 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 workshop, please install the software before arriving at the workshop. For complete installation instructions, please refer to this blog entry.

 Above: A brief video that describes Bayesian estimation for comparing two groups, and how Bayesian estimation supersedes the t test.
Why go Bayesian? Sciences from astronomy to zoology are changing from 20th-century null-hypothesis significance testing to Bayesian data analysis, because Bayesian analysis provides complete information with flexible application to numerous models. Read more:
*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 seven-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.

Recommended textbook: Doing Bayesian Data Analysis: A Tutorial with R and BUGS. The book is a genuinely accessible, tutorial introduction to doing Bayesian data analysis. The software used in the course accompanies the book, and many topics in the course are based on the book. For reviews of the book at Amazon.com, click here. See links to 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. Read more at this blog entry.