Hands-on: Bayesian Belief Networks

29 Sep 2018
15:30 - 17:00
SR 113

Hands-on: Bayesian Belief Networks

Bayesian Belief Networks for Integrated Ecological Modelling to Assess Communities and Ecosystem Services

Lecturers
Peter Goethals, PhD, Ghent University, contact: peter.goethals@ugent.be
Marie Anne Eurie Forio, PhD, Ghent University, contact: marie.forio@ugent.be

Brief description of the course content
The course aims at giving insights into the strengths and potential applications of BBN
networks to model and analyze species distributions, communities as well as ecosystem
services. The course is aimed at participants with basic ecological and modelling knowledge,
but even participants with limited computer background should be able to follow. Every
aspect of the hands-on exercises is learned from scratch, and no experience is needed with
programming or particular software packages. Slides, texts and databases will be on-line
disseminated at the start of the course.

Important is to bring a laptop, preferably will a loaded battery, on which the free version of
Netica is installed. You can download this software for free here. Versions are available for both Windows as Mac.

Schedule 15h30 – 17h
The last part will consist of a fourth exercise where the participants are requested to
develop a model from scratch related to ecosystem services trade-off modelling and
application, based on a mixture of data and information. The main idea of this exercise is to
train the participant for a real and practical situation, where many decisions need to be
made about what variables to use, how to use and combine them, etc. The participants are
free to use the provided case-study, or bring their own case and get support for that one on
the potential approach to develop and use a BBN network model. At the end of this part,
the participants can indicate how they experienced and solved the main problems along this
development path.
• Exercise 4: from text and data to BBN model and its evaluation and use
• Feedback on exercise 4: plenary discussion
• Closing discussion and feedback