Location
iDiv Leipzig, Beehive
Dates
24 – 27 February 2025, 9:30am – 4pm
Credit points
2.0 CP
Contents
The course offers a straightforward and practical approach to applied statistics using Bayesian inference for ecologists. It starts with a general introduction to statistical modeling and the concepts of Bayesian statistics (likelihood, priors, posterior distribution, MCMC sampling). We will move step-by-step from classical ANOVA and linear regression to generalized, nonlinear, or mixed-effects models, with a strong conceptual focus on the building blocks of statistical models.
While previous software required users to code in specific modeling languages (JAGS, Stan, NIMBLE), we are focusing on the user-friendly and flexible R-package ‘brms’, which makes the transition easy for people familiar with ‘lm’ or ‘lme4’. An additional introduction to coding in Stan will be provided for interested participants.
Didactic aim / competences gained
Participants learn how to practically conceptualize their research questions into statistical models. They learn how to specify and critically interpret models of varying complexity in R. The course prepares participants to analyze their own data with state-of-the-art methodology.
Prior knowledge needed
Basic knowledge in R is required (e.g. importing and transforming data). Some basic knowledge in statistics (e.g. from an introductory course) is recommended.
Participants are expected to bring their own laptops with required software installed before the course. Infos on software will be provided.


Lecturer
Dr Benjamin Rosenbaum
I moved to Computational Ecology from Applied Mathematics a decade ago and have been working in the ‘Theory in Biodiversity group’ ever since. My research focuses on statistical methods for species interactions and population dynamics. In addition to ecological modelling, I now have many years of experience in teaching and consulting in statistics.