The Bayesian belief network, once trained, can be used for classification. Based on the Bayes' theorem, which is defined in the The Bayes classification section of Chapter 3, Classification, it is defined with two parts, one directed acyclic graph and conditional probability tables (CPT) for each variable; this is in turn represented by one node in the graph and models the uncertainty by graphically representing the conditional dependencies between distinct components. The arcs in the image give a representation of causal knowledge. The interaction among the diverse sources of uncertainty is also graphically illustrated.
The uncertainty comes from various sources: