9th Summer School in Environmental Systems Analysis 2017

Eawag Environmental Systems

Target Audience

PhD students, post-doctoral researchers and senior research scientists interested in applying statistical techniques of model-based data analysis.


Mathematical models are important tools for supporting our understanding and predicting the behavior of environmental systems. However, uncertainties in data, model structure, and parameters are inevitable. The Eawag Summer School provides guidance to mathematical techniques treating such uncertainties quantitatively. It starts with elementary statistical analyses and proceeds to state of the art Bayesian computation. The summer school briefly covers model construction and sensitivity analysis and then focuses on concepts, implementation and application of Bayesian techniques for statistical inference, model prediction and uncertainty estimation.

The course is targeted at researchers who are interested in analyzing their data with mathematical models and/or in predicting future behavior of environmental systems. This includes PhD students, post-doctoral researchers, and senior research scientists working in this field.

The course consists of lectures covering the underlying theory, practice sessions based on didactical exercises, and discussion of problems of the participants. The participants are encouraged to bring their data sets and models to start working on their own problems during the course.

Emphasis is on sound statistical techniques with a focus on concepts and applications, not on mathematical derivations. Still, basic knowledge in statistics and R is required. We strongly recommend participants without a solid knowledge of probability calculus to attend the introductory lectures on Sunday, June 11, 2017.

The course will be very intense to optimize the benefit of the participants.

Learning Objectives

Provide an overview and understanding of systems analytic techniques relevant for model-based data analysis in the environmental sciences.
Get practice in applying these techniques with the mstatistics and graphics software package R and selected more specific data analysis programs.
Get advice and do first steps in analyzing the data sets of the participants.
Learn from the approaches chosen by the other participants for analyzing their data.