ABSTRACT
In this chapter, the authors aim to help reader avoid the common problems and benefit from the common solutions. They identify a small number of natural and reusable patterns in reasoning to help when building Bayesian network (BN). The authors call these patterns idioms. Idioms are a powerful method for helping to model the structure of a risk assessment problem as an individual BN; they need a different method to help us build models that "scale-up" to address large-scale problems. The cause–consequence idiom is used to model a causal process in terms of the relationship between its causes and consequences. The authors describe these multiobject BN models. They discuss the missing variable fallacy. This occurs when the authors neglect to include some crucial variable in the model and can only really reason about its absence by interrogating the model results, in terms of predictions and inferences.
