A Bayesian Belief Network (BBN) 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. This 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.