Doing Bayesian Data Analysis
Many fields of science are transitioning from null hypothesis significance testing (NHST) to Bayesian data analysis. Bayesian analysis provides rich information about the relative credibilities of all candidate parameter values for any descriptive model of the data, without reference to p values. 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. (More about why to go Bayesian is described below.) This workshop shows you how to do Bayesian data analysis, hands on, with free software called R and JAGS. The workshop will use new programs and examples.
This workshop is offered through the University of Edinburgh. Registration is required and links are provided below.
Workshop Objectives: You will learn
Workshop Audience: The intended audience is advanced students, faculty, and other researchers, from all disciplines, who want a ground-floor introduction to doing Bayesian data analysis.
Workshop Prerequisites: No specific mathematical expertise is presumed. In particular, no matrix algebra is used in the workshop. Some previous familiarity with statistical methods such as a t-test or linear regression can be helpful, as is some previous experience with programming in any computer language, but these are not critical.
Workshop Topics: (Exact content, ordering, and durations may change.)
A brief video that describes Bayesian estimation for comparing two groups, and how Bayesian estimation supersedes the t test. |
See also this introductory chapter.
Who is the instructor?
John Kruschke is eight-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 acclaimed introductory textbook on Bayesian data analysis and many tutorial articles. He has given numerous popular workshops on Bayesian methods. His research interests include the science of moral judgment and Bayesian data analysis. He received the Troland Research Award from the National Academy of Sciences, and the Remak Distinguished Scholar Award from Indiana University. He has been on the editorial boards of several scientific journals, including Psychological Review, the Journal of Experimental Psychology: General, the Journal of Mathematical Psychology, and others.
Highly recommended textbook:
Doing Bayesian Data Analysis, 2nd Edition: A Tutorial with R, JAGS, and Stan. The book is a genuinely accessible, tutorial introduction to doing Bayesian data analysis. The software used in the workshop accompanies the book, and many topics in the workshop are based on the book. (The workshop uses the 2nd edition, not the 1st edition.) Further information about the book can be found here.
Register for the workshop.
This workshop is offered through the University of Edinburgh. You must register to attend. Complete registration and contact information is at this link. The instructor has no control of fees or registration procedure.
Install software before arriving.
It is important to bring a notebook computer to the workshop, so you can run the programs and see how their output corresponds with the presentation material. Please install the software before arriving at the workshop. The software and programs are occasionally updated, so please check here a week before the workshop to be sure you have the most recent versions. For complete installation instructions, please refer to this web page.
|Two historical trends in data analysis, that converge on the methods presented in this workshop. (NHST = null hypothesis significance testing. MLE = maximum likelihood estimation.)|
Sciences from astronomy to zoology are changing from null-hypothesis significance testing to Bayesian data analysis, because Bayesian analysis provides richer information with great flexibility and without need for p values. Read more:
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