Introduction to Bayesian Statistics Using R and Stan for Ecologists

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). Based on textbook examples, the main focus is coding and discussing statistical models using the Stan environment in R. We will move step-by-step from basic ANOVA or linear regression to generalized, nonlinear, or mixed-effects 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.

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.