Email this article 
Hide
Show all
[test by reto]
| 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:: 3228. Submitted: 2017-03-07. Published: 2019-11-07. |
|||||||
| Paper: |
Learning Large-Scale Bayesian Networks with the sparsebn Package
Download PDF
(Downloads: 1346)
|
||||||
| Supplements: |
| ||||||
| DOI: |
10.18637/jss.v091.i11
|
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. |