Bayesian Networks (BNs) are a flexible modelling method that can be used in various ways to address different types of research questions. Their strength comprises the ability to combine different types of data and knowledge, as well as include, integrate and represent uncertainty. The graphical nature of BNs makes them easy to communicate across scientific disciplines, and to diverse groups of stakeholders.
The objective of the course is to learn how Bayesian Networks can be used in analyzing socio-ecological systems. The course introduces the theoretical background and principles of BNs and probabilistic modelling. Major part of the course will be covered by a collaborative case-study in which we go through the modelling steps from problem framing to model construction and parameterization.
After the course, the student can:
Laura has been working with Bayesian networks for 20 years, and has degrees both in aquatic ecology and computer science. She works as a leading researcher in Finnish Environment Institute, and her current interests include decision support systems, interdisciplinary research, and machine learning.Päivi Haapasaari, University of Helsinki, Finland.
Päivi works as a professor in multidisciplinary risk analysis
at the University of Helsinki. Her academic background is in environmental
social sciences. Her research covers social scientific and
inter/transdisciplinary approaches to marine environmental problems and
resource use, using e.g. Bayesian networks.