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Nonparanormal Bayesian Learning of Directed Acyclic Graphs under Gamma and Inverse-Gamma Innovation Priors: Closed-Form Scores and Informed Sampling
Bayesian structure learning for directed acyclic graphs (DAGs) is a central tool for reconstructing biological networks, yet it often assumes the data are jointly Gaussian. In motivating proteomic applications, this assumption is routinely violated by skewed and heavy-tailed data, causing Gaussian D
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