---
title: "Adaptive Thresholding in Structure Learning of a Bayesian Network"
authors:
  - "Boaz Lerner"
  - "Michal Afek"
  - "Rafi Bojmel"
doi: ""
url: "https://ijcai.org/proceedings/2013/218"
pdf: "https://ijcai.org/proceedings/2013/0218.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "1458-1463"
track: ""
keywords:
---

# Adaptive Thresholding in Structure Learning of a Bayesian Network

Boaz Lerner, Michal Afek, Rafi Bojmel

*Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence*, pages 1458-1463.

## Abstract

Thresholding a measure in conditional independence (CI) tests using a fixed value enables learning and removing edges as part of learning a Bayesian network structure. However, the learned structure is sensitive to the threshold that is commonly selected: 1) arbitrarily; 2) irrespective of characteristics of the domain; and 3) fixed for all CI tests. We analyze the impact on mutual information – a CI measure – of factors, such as sample size, degree of variable dependence, and variables' cardinalities. Following, we suggest to adaptively threshold individual tests based on the factors. We show that adaptive thresholds better distinguish between pairs of dependent variables and pairs of independent variables and enable learning structures more accurately and quickly than when using fixed thresholds.