Bayesian Data Analysis (GSERM Ljubljana 2024)

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.

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. This course is closely modeled on the very successful series of Workshops given by Prof. John Kruschke.

Course leader

Ryan Bakker from University of Essex, United Kingdom

Target group

The intended audience is advanced students, faculty, and other researchers, from all disciplines, who want a ground-floor introduction to doing Bayesian data analysis.

Course aim

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. This course is closely modeled on the very successful series of Workshops given by Prof. John Kruschke.

Course Objectives:
- The rich information provided by Bayesian analysis and how it differs from traditional (Frequentist) statistical analysis
- The concepts of Bayesian reasoning along with the easy math and intuitions for Bayes’ rule
- The concepts and hands-on use of modern algorithms (“Markov chain Monte Carlo”) that achieve Bayesian analysis for realistic applications
- How to use the free software R and JAGS for Bayesian analysis, readily useable and adaptable for your research applications
- An extensive array of applications, including comparison of two groups, ANOVA-like designs, linear regression, and logistic regression.
- How to apply Bayesian estimation to hierarchical (multi-level) models. See more details in the list of topics, below.
- What to look for when doing data analysis in a variety of other software settings, including SPSS, SAS, JASP, M-Plus and packages like brms or r-stan-arm in R.

Credits info

4 EC
At the end of the course, participants receive a Certificate of Attendance and a Transcript of Records (if taking part in final examination).

Fee info

CHF 1000: Flat early bird discount worth CHF 100.00 (valid until 31 October 2023)

Scholarships

No.