Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses

Recently, Liu et al. [6, 7] introduced the notion of a nonparanormal distribution, which generalizes Instead of only analyzing the standard covariance matrix, we show that it is often fruitful to augment the usual covariance matrix with higher-order interaction terms. Other related work on graphical model selection for discrete graphs includes the Classic Chow-Liu algorithm for trees [8]; nodewise logistic regression for discrete models with pairwise interactions [9, 10]; and techniques based on conditional entropy or mutual information [11, 12].