Causal Discovery and Reasoning for Continuous Variables with an Improved Bayesian Network Constructed by Locality Sensitive Hashing and Kernel Density Estimation

Causal Discovery and Reasoning for Continuous Variables with an Improved Bayesian Network Constructed by Locality Sensitive Hashing and Kernel Density Estimation

The structure learning of a Bayesian network (BN) is a crucial process that aims to unravel the complex dependencies relationships among variables using a given dataset. This paper proposes a new BN structure learning method for data with continuous attribute values. As a non-parametric distribution...

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Bibliographic Details
Main Authors: Chenghao Wei, Chen Li, Yingying Liu, Song Chen, Zhiqiang Zuo, Pukai Wang, Zhiwei Ye
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Entropy
Subjects:
Bayesian network
kernel density estimation
locality sensitive hashing
hybrid-based structure learning
mutual information
conditional mutual information
Online Access:https://www.mdpi.com/1099-4300/27/2/123

Internet

https://www.mdpi.com/1099-4300/27/2/123

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