A BBN is a probabilistic graphical model (a type of statistical model) that represents a set of random variables and their conditional dependencies via a directed acyclic graph. As a graphical structure BBN allows for the representation, of and reasoning about, an uncertain situation. The nodes in a network represent a set of variables in the domain being modelled. The nodes are connected by links representing the relationship between variables. These relationships can be learned from the data if these are available or can be elicited from experts in the field. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. BBNs can answer ‘how’ questions by selecting a desired outcome and looking at how the independent variables are changed. ‘What-if’ scenarios can also be tested by changing predictor variables to states expected in the future, then observing changes in dependent variables. Any information supplied to the network will update the probabilities throughout the network immediately, and the strength of the prediction can be judged by the probability value for a given outcome.


Bayesian Belief Networks (BBN’s) provide a useful integrative tool for linking science knowledge on components of complex land-water-social systems to explore scenarios to optimise benefits of different practices on environmental, economic and social values.

State of Development Please Select

Development Contact


Outcome Areas Economic, Environmental, Social, Cultural
Management Domains Land, Community, Air Quality, Coastal, Urban Systems, Natural Hazards, Biodiversity, Waste, Freshwater
Steady State or Dynamic Unknown
Level of Integration Economic, Environmental, Social, Cultural

Input & Output Data


Open/Closed Source Open Source

User Information

User Interface Please Select
Ease of Use Moderate
Use in Policy Process Plan (Policy Formulation), Review (Issue Identification)

Technical Considerations

Analytical Techniques Bayesian Belief Network
Model Structure


Keywords land, water, social, economic, environmental

For a range of software packages see -

Key References

Bayseian Belief Networks - CSIRO Paper 2003 - see PDF

Associated Case Studies