Semiparametric Bayesian structure learning of nonparanormal directed acyclic graphs with local--global shrinkage

Bayesian structure learning of directed acyclic graphs (DAGs) is central to high-dimensional causal discovery, yet existing methods mostly assume multivariate Gaussian data, which is routinely violated by measurements displaying heavy tails, skewness, or bounded support. We address this by introduci