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• W2952352874 abstract "In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesian networks from data. Our results apply whenever the learning algorithm uses a scoring criterion that favors the simplest model able to represent the generative distribution exactly. Our results therefore hold whenever the learning algorithm uses a consistent scoring criterion and is applied to a sufficiently large dataset. We show that identifying high-scoring structures is hard, even when we are given an independence oracle, an inference oracle, and/or an information oracle. Our negative results also apply to the learning of discrete-variable Bayesian networks in which each node has at most k parents, for all k > 3." @default.
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• W2952352874 date "2012-10-19" @default.
• W2952352874 modified "2023-09-27" @default.
• W2952352874 title "Large-Sample Learning of Bayesian Networks is NP-Hard" @default.
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