Model Selection for Dags via RJMCMC for the Discrete and Mixed Case

Based on a reversible jump Markov Chain Monte Carlo (RJMCMC)
algorithm which was developed by Fronk and Giudici (2000) to deal
with model selection for Gaussian dags, we propose a new approach
for the pure discrete case. Here, the main idea is to introduce
latent variables which then allow to fall back on the already
treated continuous case. This makes it also straightforward to
tackle the mixed case, i.e. to deal simultaneously with continuous
and discrete variables. The performance of the approach is
investigated by means of a simulation study for different standard
situations. In addition, a real data application is provided.