Learning Large-Scale Bayesian Networks with the sparsebn Package
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 deve...
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doaj-a4c4d9ee9de1456bac121db8abdc78142020-11-25T02:50:33ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602019-11-0191113810.18637/jss.v091.i111328Learning Large-Scale Bayesian Networks with the sparsebn PackageBryon AragamJiaying GuQing ZhouLearning 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.https://www.jstatsoft.org/index.php/jss/article/view/3051bayesian networkscausal networksgraphical modelsmachine learningstructural equation modelingmulti-logit regressionexperimental data |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Bryon Aragam Jiaying Gu Qing Zhou |
spellingShingle |
Bryon Aragam Jiaying Gu Qing Zhou Learning Large-Scale Bayesian Networks with the sparsebn Package Journal of Statistical Software bayesian networks causal networks graphical models machine learning structural equation modeling multi-logit regression experimental data |
author_facet |
Bryon Aragam Jiaying Gu Qing Zhou |
author_sort |
Bryon Aragam |
title |
Learning Large-Scale Bayesian Networks with the sparsebn Package |
title_short |
Learning Large-Scale Bayesian Networks with the sparsebn Package |
title_full |
Learning Large-Scale Bayesian Networks with the sparsebn Package |
title_fullStr |
Learning Large-Scale Bayesian Networks with the sparsebn Package |
title_full_unstemmed |
Learning Large-Scale Bayesian Networks with the sparsebn Package |
title_sort |
learning large-scale bayesian networks with the sparsebn package |
publisher |
Foundation for Open Access Statistics |
series |
Journal of Statistical Software |
issn |
1548-7660 |
publishDate |
2019-11-01 |
description |
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. |
topic |
bayesian networks causal networks graphical models machine learning structural equation modeling multi-logit regression experimental data |
url |
https://www.jstatsoft.org/index.php/jss/article/view/3051 |
work_keys_str_mv |
AT bryonaragam learninglargescalebayesiannetworkswiththesparsebnpackage AT jiayinggu learninglargescalebayesiannetworkswiththesparsebnpackage AT qingzhou learninglargescalebayesiannetworkswiththesparsebnpackage |
_version_ |
1724737920636551168 |