# Globally optimal score-based learning of directed acyclic graphs in high-dimensions

Bryon AragamArash AminiQing Zhou

We prove that $\Omega(s\log p)$ samples suffice to learn a sparse Gaussian directed acyclic graph (DAG) from data, where $s$ is the maximum Markov blanket size. This improves upon recent results that require $\Omega(s^{4}\log p)$ samples in the equal variance case... (read more)

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