# Structure Learning in Bayesian Networks of Moderate Size by Efficient Sampling

arXiv: https://arxiv.org/abs/1501.04370 · HTML: https://mlsanity.com/paper/1501.04370/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2015.

## Built on by (most cited first, 3 of 3)

- [DAGs with No Curl: An Efficient DAG Structure Learning Approach](https://mlsanity.com/paper/2106.07197/) (2021)
- [DAG-WGAN: Causal Structure Learning With Wasserstein Generative Adversarial Networks](https://mlsanity.com/paper/2204.00387/) (2022)
- [Causality Learning With Wasserstein Generative Adversarial Networks](https://mlsanity.com/paper/2206.01496/) (2022)

## Abstract (arXiv)

We study the Bayesian model averaging approach to learning Bayesian network structures (DAGs) from data. We develop new algorithms including the first algorithm that is able to efficiently sample DAGs according to the exact structure posterior. The DAG samples can then be used to construct estimators for the posterior of any feature. We theoretically prove good properties of our estimators and empirically show that our estimators considerably outperform the estimators from the previous state-of-the-art methods.