倡导使用贝叶斯网络来分析煤炭中元素的发生方式
1College of Geoscience and Survey Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
ACS omega
|October 30, 2023
概括
了解煤炭中的元素发生方式对于环境和健康评估至关重要. 彼得-克拉克 (PC) 算法提供了一种优越的方法来分析煤炭元素数据,与等级聚类相比.
科学领域:
- 地质化学 地质化学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 煤中元素的发生方式对于理解无机起源,环境影响和沉积过程至关重要.
- 统计分析,特别是层次聚类,是常用的,但可以产生误导性的结果,重点是元素集群而不是单个元素.
研究的目的:
- 介绍和评估应用彼得 - 克拉克 (PC) 算法来推断元素在煤中的发生模式.
- 解决在分析煤炭元素数据时等级聚类的局限性.
主要方法:
- 利用了彼得 - 克拉克 (PC) 算法的初始阶段,这是贝叶斯网络结构学习方法.
- 将PC算法应用于来自中国内蒙古的95个古老时代晚期煤炭样本的数据集.
主要成果:
- 该PC算法为煤炭元素数据提供了重要的和意想不到的见解.
- 证明了PC算法对等级集群的优越性,用于分析煤炭中元素发生模式.
结论:
- 计算机算法是一个强大的工具,用于探索煤炭元素数据中的关系.
- 这种方法提供了一个更准确的方法来确定煤炭中元素的存在方式,这对环境和健康研究有影响.
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