# Learning Discrete Bayesian Networks from Continuous Data

arXiv: https://arxiv.org/abs/1512.02406 · HTML: https://mlsanity.com/paper/1512.02406/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2015.

## Built on by (most cited first, 6 of 6)

- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [Why did I fail? A Causal-based Method to Find Explanations for Robot Failures](https://mlsanity.com/paper/2204.04483/) (2022)
- [Learning Discretized Bayesian Networks with GOMEA](https://mlsanity.com/paper/2402.12175/) (2024)
- [Maximum likelihood inference for high-dimensional problems with multiaffine variable relations](https://mlsanity.com/paper/2409.03495/) (2024)
- [An Experimental Study on Fairness-aware Machine Learning for Credit Scoring Problems](https://mlsanity.com/paper/2412.20298/) (2024)
- [Conditional Independence Tests for Constraint-Based Causal Discovery: A Survey](https://mlsanity.com/paper/2608.11156/) (2026)

## Abstract (arXiv)

Learning Bayesian networks from raw data can help provide insights into the relationships between variables. While real data often contains a mixture of discrete and continuous-valued variables, many Bayesian network structure learning algorithms assume all random variables are discrete. Thus, continuous variables are often discretized when learning a Bayesian network. However, the choice of discretization policy has significant impact on the accuracy, speed, and interpretability of the resulting models. This paper introduces a principled Bayesian discretization method for continuous variables in Bayesian networks with quadratic complexity instead of the cubic complexity of other standard techniques. Empirical demonstrations show that the proposed method is superior to the established minimum description length algorithm. In addition, this paper shows how to incorporate existing methods into the structure learning process to discretize all continuous variables and simultaneously learn Bayesian network structures.