Bayesian network models with discrete and continuous variables

Bayesian networks are powerful tools for handling problems which are specified through a multivariate probability distribution. A broad background of theory and methods have been developed for the case in which all the variables are discrete. However, situations in which continuous and discrete variables coexist in the same problem are common in practice. In such cases, usually the continuous variables are discretized and therefore all the existing methods for discrete variables can be applied, but the price to pay is that the obtained model is just an approximation. In this chapter we study two frameworks where continuous and discrete variables can be handled simultaneously without using discretization. These models are based on the CC and MTE distributions.