文章基本信息

标题：Learning Large-Scale Bayesian Networks with the sparsebn Package
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作者：Bryon Aragam ; Jiaying Gu ; Qing Zhou 等
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2019
卷号：91
期号：1
页码：1-38
DOI：10.18637/jss.v091.i11
出版社：University of California, Los Angeles
摘要：Learning graphical models from data is an important problem with wide applications, ranging from genomics to the social sciences. Nowadays datasets often have upwards of thousands - sometimes tens or hundreds of thousands - of variables and far fewer samples. To meet this challenge, we have developed a new R package called sparsebn for learning the structure of large, sparse graphical models with a focus on Bayesian networks. While there are many existing software packages for this task, this package focuses on the unique setting of learning large networks from high-dimensional data, possibly with interventions. As such, the methods provided place a premium on scalability and consistency in a high-dimensional setting. Furthermore, in the presence of interventions, the methods implemented here achieve the goal of learning a causal network from data. Additionally, the sparsebn package is fully compatible with existing software packages for network analysis.
关键词：Bayesian networks; causal networks; graphical models; machine learning; structural equation modeling; multi-logit regression; experimental data.
其他关键词：Bayesian networks;causal networks;graphical models;machine learning;structural equation modeling;multi-logit regression;experimental data