文章基本信息

标题：Improved Parameter Uniform Priors in Bayesian Network Structure Learning
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作者：Manxi Wang ; Liandong Wang ; Zidong Wang 等
期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：252
期号：4
页码：1-7
DOI：10.1088/1755-1315/252/4/042099
出版社：IOP Publishing
摘要：Bayesian Dirichlet equivalent uniform score (BDeu) is often used in Bayesian structure learning. But it does not work well when data size is sparse because the equivalence of the prior parameter distribution isn't suit for the specific data set. To break the rules of uniform and equivalent, the paper proposes the Bayesian Dirichlet Sparse score (BDs) which change distribution of prior parameter through the all zero items in the sparse data. The circulation principle of information entropy and simulations are used to explain the reason why BDs is better than BDeu when data size is sparse. In the experiments, we also verify the stability of BDs when hyperparameters change.