Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity

NeurIPS 2017 Asish GhoshalJean Honorio

Learning the directed acyclic graph (DAG) structure of a Bayesian network from observational data is a notoriously difficult problem for which many hardness results are known. In this paper we propose a provably polynomial-time algorithm for learning sparse Gaussian Bayesian networks with equal noise variance --- a class of Bayesian networks for which the DAG structure can be uniquely identified from observational data --- under high-dimensional settings... (read more)

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