Introduction to Bayesian statistics in R

The course offers a straightforward and practical approach to applied statistics using Bayesian inference. It starts with a gentle introduction to the concepts of Bayesian statistics (priors, likelihood, posterior distribution, MCMC sampling). Participants will both learn how to code models in the Stan environment and also get to know the user-friendly package 'brms'. We will move step-by-step from basic ANOVA or linear regression to generalized, nonlinear, or mixed-effects models with a strong focus on the building blocks of statistical models.

Didactic aims/ competencies gained
Participants learn how to practically think in terms like data, model, likelihood, parameters, predictions. They learn how to specify and code statistical models of varying complexity in R. While the 'brms' package offers an easy transition from classical 'lm' or 'lme4' modeling, additional knowledge of 'Stan' code allows participants to adjust models to their specific research questions.

Prior knowledge needed
Basic knowledge in R and statistics, e.g. importing and transforming data, performing basic linear regression using “lm”.

Participants are expected to participate actively in the hands-on training and to bring their own laptops.