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Optimal estimation of Gaussian DAG models. (arXiv:2201.10548v1 [math.ST])
Jan. 26, 2022, 2:11 a.m. | Ming Gao, Wai Ming Tai, Bryon Aragam
cs.LG updates on arXiv.org arxiv.org
We study the optimal sample complexity of learning a Gaussian directed
acyclic graph (DAG) from observational data. Our main result establishes the
minimax optimal sample complexity for learning the structure of a linear
Gaussian DAG model with equal variances to be $n\asymp q\log(d/q)$, where $q$
is the maximum number of parents and $d$ is the number of nodes. We further
make comparisons with the classical problem of learning (undirected) Gaussian
graphical models, showing that under the equal variance assumption, these …
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