A Bayesian Belief Network is a framework that uses a graphical representation to show the flow of information in a system. It has nodes or vertices to represent variables which can include observed quantities, latent (unobserved) quantities, expert opinion, model outputs, or unknown parameters. There are links or edges joining parent nodes to child nodes. The difference between this and other similar frameworks is in the use of conditional probabilities to express the relationships between nodes.

Aim of the resource: 
<p>Allows the building of complex networks from simple segments and it enables uncertainties to be assessed at every stage, so the outcomes of the network reflect the weight of the evidence that supports the conclusion.</p>
Scope
Scale of application: 
Global
Regional
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National
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Practical information
UN languages in which the resource is available: 
Development stage: 
Full, working product
Contact details
Contact Name (Person or group/organization): 
HUGIN