  Bayesian networks have been used as a mechanism to represent the joint
distribution of multiple random variables in a flexible yet interpretable
manner. One major challenge in learning the structure of a Bayesian network is
how to model networks which include a mixture of continuous and discrete random
variables, known as hybrid Bayesian networks. This paper overviews the
literature on approaches to handle hybrid Bayesian networks. Typically one of
two approaches is taken: either the data are considered to have a joint
distribution which is designed for a mixture of discrete and continuous
variables, or continuous random variables are discretized, resulting in
discrete Bayesian networks. In this paper, we propose a strategy to model all
random variables as Gaussian, referred to it as {\it Run it As Gaussian (RAG)}.
We demonstrate that RAG results in more reliable estimates of graph structures
theoretically and by simulation studies, than converting continuous random
variables to discrete. Both strategies are also implemented on a childhood
obesity data set. The two different strategies give rise to significant
differences in the optimal graph structures, with the results of the simulation
study suggesting that our strategy is more reliable.
