  Structure learning of Gaussian graphical models is an extensively studied
problem in the classical multivariate setting where the sample size n is larger
than the number of random variables p, as well as in the more challenging
setting when p>>n. However, analogous approaches for learning the structure of
graphical models with mixed discrete and continuous variables when p>>n remain
largely unexplored. Here we describe a statistical learning procedure for this
problem based on limited-order correlations and assess its performance with
synthetic and real data.
