Learning Large-Scale Bayesian Networks with the sparsebn Package

doi:10.18637/jss.v091.i11

Learning Large-Scale Bayesian Networks with the sparsebn Package

2019-01-01

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AI

DOI：10.18637/jss.v091.i11

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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.

Keyword:

Bayesian networks

causal networks

graphical models

machine learning

structural equation modeling

multi-logit regression

experimental data

Learning Large-Scale Bayesian Networks with the sparsebn Package

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