Statistical Methods Course Description
Overview: 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 course shows you how to do Bayesian data analysis, hands on, with free software called R and JAGS. The course will use new programs and examples.
Mike Kalish is a Professor of Psychology at Syracuse University. He earned a PhD in Cognitive Science from UC San Diego and has held academic positions in both the US and Australia. His primary research area is the psychology of category learning, which has led him to work on areas of methodology including State-Trace Analysis and Bayesian estimation and model selection. He frequently teaches postgraduate level courses on Bayesian statistics.
The intended audience is advanced students, faculty, and other researchers, from all disciplines, who want a groundfloor introduction to doing Bayesian data analysis.
Below are links to course files for those who enrolled in the course. Please download these files onto your computer before the first day of the course. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it.
After May 1st: Click Here to Access Bayesian Data Analysis Course Files