# An Algorithm for the Construction of Bayesian Network Structures from Data

arXiv: https://arxiv.org/abs/1303.1485 · HTML: https://mlsanity.com/paper/1303.1485/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2013.

## Built on by (most cited first, 5 of 5)

- [Score matching enables causal discovery of nonlinear additive noise models](https://mlsanity.com/paper/2203.04413/) (2022)
- [A survey of Bayesian Network structure learning](https://mlsanity.com/paper/2109.11415/) (2021)
- [Order-based Structure Learning with Normalizing Flows](https://mlsanity.com/paper/2308.07480/) (2023)
- [Neural Graph Revealers](https://mlsanity.com/paper/2302.13582/) (2023)
- [I-FLOP: Fast Learning of Order and Parents from Interventional Data](https://mlsanity.com/paper/2608.28245/) (2026)

## Abstract (arXiv)

Previous algorithms for the construction of Bayesian belief network structures from data have been either highly dependent on conditional independence (CI) tests, or have required an ordering on the nodes to be supplied by the user. We present an algorithm that integrates these two approaches - CI tests are used to generate an ordering on the nodes from the database which is then used to recover the underlying Bayesian network structure using a non CI based method. Results of preliminary evaluation of the algorithm on two networks (ALARM and LED) are presented. We also discuss some algorithm performance issues and open problems.