ABSTRACT
In this chapter, we will cover how discrete and Gaussian Bayesian networks can be combined to create a conditional Gaussian Bayesian network (CGBN). A CGBN is a “mixture of normals” model in which continuous nodes can have both continuous and discrete parents, while discrete nodes can only have discrete parents. We see this as an initial step towards the more complex BNs presented in Chapters 4 and 5, which provide even greater flexibility. We will model healthcare costs as an example, using public data from UK's National Health Service.
