Language: English
Lead: Carlo Albert, Andreas Scheidegger

Mathematical models are essential for gaining insights into environmental systems and predicting their behavior. This summer school provides guidance on how to use Bayesian techniques to treat uncertainties in data, model structure, and parameters quantitatively. The course is designed for PhD students, post-doctoral researchers, and senior research scientists from all disciplines who utilize mathematical models in their work, with a focus on examples from hydrology and ecology.

The course consists of lectures and practice sessions with didactical exercises with solutions in R, Julia, and Python. We will also have time to discuss your individual modeling challenges. Emphasis is on the concepts and applications of methods, not on mathematical derivations. To profit most from the course, you should feel comfortable with basic probability calculus, and reading and writing simple code. For those in need of a refresher, optional lectures on the fundamentals of probability calculus and an introduction to R programming are available on Sunday.

More information