In summary: why should we use BBNs?

 BBNs on their own enable us to model uncertain events and arguments about them. The intuitive visual representation can be very useful in clarifying previously opaque assumptions or reasonings hidden in the head of an expert. With BBNs, it is possible to articulate expert beliefs about the dependencies between different variables and BBNs allow an injection of scientific rigour when the probability distributions associated with individual nodes are simply 'expert opinions'. BBNs can expose some of the common fallacies in reasoning due to misunderstanding of probability.

However, the real power of BBNs comes when we apply the rules of Bayesian probability to propagate consistently the impact of evidence on the probabilities of uncertain outcomes. A BBN will derive all the implications of the beliefs that are input to it; some of these will be facts that can be checked against observations, or simply against the experience of the decision makers themselves.