Weka's Bayesian networks
"assume that all variables are discrete"[Weka] p.22 and
"a limitation of the current classes is that they
assume that there are no missing values"[Weka] p.23.

In Weka, continuous variables must be discretised first and
the way this is done may affect the outcome.
This is unnecessary for modelling and, for splitting, is
part of the network optimisation when using
our [IP] classification trees.

[Weka] R. R. Bouckaert. Bayesian networks in Weka.
TR 14/2004, Comp. Sci. Dept.. U. of Waikato, Sept. 2004.