# A Multivariate Discretization Method for Learning Bayesian Networks from Mixed Data

arXiv: https://arxiv.org/abs/1301.7403 · HTML: https://mlsanity.com/paper/1301.7403/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2013.

## Built on by (most cited first, 1 of 1)

- [Maximum likelihood inference for high-dimensional problems with multiaffine variable relations](https://mlsanity.com/paper/2409.03495/) (2024)

## Abstract (arXiv)

In this paper we address the problem of discretization in the context of learning Bayesian networks (BNs) from data containing both continuous and discrete variables. We describe a new technique for <EM>multivariate</EM> discretization, whereby each continuous variable is discretized while taking into account its interaction with the other variables. The technique is based on the use of a Bayesian scoring metric that scores the discretization policy for a continuous variable given a BN structure and the observed data. Since the metric is relative to the BN structure currently being evaluated, the discretization of a variable needs to be dynamically adjusted as the BN structure changes.