Bayesian networks (BNs) are ideal models for natural resource management as they are able to represent complex natural systems, integrate different sources and types of information and investigate alternative management and system change scenarios.

Increasingly, BNs are being used in Natural Resource Management (NRM) applications in Australia, including water and climate related issues. They also have a long history being applied in other fields, such as medicine and engineering.

In this course we seek to provide a balance between theory and practice for developing and applying BNs within NRM. Existing BN models, built for NRM applications, will be used to illustrate theoretical concepts.

Key components of the course are insights into ongoing research being undertaken in iCAM.

Note: Graduate students attend joint classes with undergraduates but are assessed separately.

On satisfying the requirements of this course, students will have the knowledge and skills to:

1. Explain the theoretical concepts underpinning Bayesian networks and the participatory processes required for model building
2. Integrate qualitative and quantitative data and other model forms of evidence into Bayesian networks, and apply these to integration, risk, climate change and conservation assessments
3. Integrate Bayesian networks and Geographic Information Systems, and develop practical skills in building, evaluating and using Bayesian networks for natural resource management purposes
4. Critically analyse best practice in building and implementing Bayesian networks for natural resource management purposes
5. Systemise knowledge and structure problems for a natural resource management issue that is the focus of model building 

Assessment will be based on:

• 3000-word literature review (25%; LO 1, 5)
• Short presentation explaining the construction of a Bayesian network (15%; LO 2, 3,4)
• 3000-word final report on building a specific Bayesian network (60%; LO 2, 3, 4, 5)

Autumn session.  65 hours of contact taught as a two-week block course, comprising lectures and practical components. 

Bachelor degree; general science knowledge.

Cain JD. 2001. Planning improvements in natural resources management: Guidelines for using Bayesian Networks to support the planning and management of development programmes in the water sector and beyond. Wallingford, UK: Centre for Ecology and Hydrology. Available at http://www.norsys.com/resources.htm