# Learning Bayesian Networks from Incomplete Data with Stochastic Search Algorithms

arXiv: https://arxiv.org/abs/1301.6726 · HTML: https://mlsanity.com/paper/1301.6726/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2013.

## Built on by (most cited first, 2 of 2)

- [Fast Parallel Bayesian Network Structure Learning](https://mlsanity.com/paper/2212.04259/) (2022)
- [Preserving Data Privacy in Learning Causal Structure with Fully Homomorphic Encryption](https://mlsanity.com/paper/2606.05129/) (2026)

## Abstract (arXiv)

This paper describes stochastic search approaches, including a new stochastic algorithm and an adaptive mutation operator, for learning Bayesian networks from incomplete data. This problem is characterized by a huge solution space with a highly multimodal landscape. State-of-the-art approaches all involve using deterministic approaches such as the expectation-maximization algorithm. These approaches are guaranteed to find local maxima, but do not explore the landscape for other modes. Our approach evolves structure and the missing data. We compare our stochastic algorithms and show they all produce accurate results.