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Bayesian Structure Learning by Recursive Bootstrap
[article]
2018
arXiv
pre-print
We address the problem of Bayesian structure learning for domains with hundreds of variables by employing non-parametric bootstrap, recursively. We propose a method that covers both model averaging and model selection in the same framework. The proposed method deals with the main weakness of constraint-based learning---sensitivity to errors in the independence tests---by a novel way of combining bootstrap with constraint-based learning. Essentially, we provide an algorithm for learning a tree,
arXiv:1809.04828v1
fatcat:ix7zigjs2fg5pdtiyu7ktd3efe