|Authors:||Bryon Aragam, Jiaying Gu, Qing Zhou|
|Title:||Learning Large-Scale Bayesian Networks with the sparsebn Package|
|Abstract:||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.|
Page views:: 3176. Submitted: 2017-03-07. Published: 2019-11-07.
Learning Large-Scale Bayesian Networks with the sparsebn Package
This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.