# Learning Large-Scale Bayesian Networks with the sparsebn Package

arXiv: https://arxiv.org/abs/1703.04025 · HTML: https://mlsanity.com/paper/1703.04025/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2017.

## Built on by (most cited first, 10 of 10)

- [Distributed Learning of Generalized Linear Causal Networks](https://mlsanity.com/paper/2201.09194/) (2022)
- [Partitioned hybrid learning of Bayesian network structures](https://mlsanity.com/paper/2103.12188/) (2021)
- [Sequentially learning the topological ordering of causal directed acyclic graphs with likelihood ratio scores](https://mlsanity.com/paper/2202.01748/) (2022)
- [Learning Large DAGs by Combining Continuous Optimization and Feedback Arc Set Heuristics](https://mlsanity.com/paper/2107.00571/) (2021)
- [Learning Sparse Fixed-Structure Gaussian Bayesian Networks](https://mlsanity.com/paper/2107.10450/) (2021)
- [Learning complex dependency structure of gene regulatory networks from high dimensional micro-array data with Gaussian Bayesian networks](https://mlsanity.com/paper/2106.15365/) (2021)
- [A Stochastic Variance-Reduced Coordinate Descent Algorithm for Learning Sparse Bayesian Network from Discrete High-Dimensional Data](https://mlsanity.com/paper/2108.09501/) (2021)
- [Learning Multitask Gaussian Bayesian Networks](https://mlsanity.com/paper/2205.05343/) (2022)
- [BigBraveBN: algorithm of structural learning for bayesian networks with a large number of nodes](https://mlsanity.com/paper/2208.10312/) (2022)
- [An Asymptotically Optimal Coordinate Descent Algorithm for Learning Bayesian Networks from Gaussian Models](https://mlsanity.com/paper/2408.11977/) (2024)

## Abstract (arXiv)

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.