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Updated: May 25, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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连续变量的因果发现和推理,通过局部敏感哈希和内核密度估计构建的改进的贝叶斯网络

Chenghao Wei1, Chen Li1, Yingying Liu1

  • 1School of Computer Science, Hubei University of Technology, Wuhan 430068, China.

Entropy (Basel, Switzerland)
|February 26, 2025
PubMed
概括

本研究介绍了一种高效的贝叶斯网络 (BN) 结构学习方法,使用核心密度估计 (KDE) 与局部敏感散列 (LSH). 这种方法提高了连续数据的计算速度和准确性,优于传统方法.

关键词:
贝叶斯网络是一个贝叶斯网络.条件是有条件的.有条件的相互信息.基于混合结构的学习学习结构.核密度估计核密度估计局部敏感的哈希处理这是相互信息的互惠.

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