
3
More Complex Cases: Hybrid Bayesian
Networks
In Chapters 1 and 2 we considered BNs with either discrete or continuous
variables. Moreover, in each BN all variables followed probability distributions
belonging to the same family: multinomial or multivariate normal. In the
following, we would like to show that there are no theoretical reasons for such
restrictions and that, according to the phenomenon unde r investigation:
1. we can mix discrete and continuous variables and
2. we can use any kind of distribution.
Unfortunately, this increase in flexibility means BNs become more compli-
cated, and no dedicated R package exists to handle them. Therefore, ...