图形迪里克莱特过程用于聚类不可交换的分组数据
Arhit Chakrabarti1, Yang Ni2, Ellen Ruth A Morris3
1Department of Statistics, Texas A&M University, College Station, TX 77843-3143, USA.
概括
本研究介绍了用于集群不可交换的分组数据的图形狄里克莱特过程. 这种贝叶斯式方法使得跨依赖组的集群共享成为可能,改善了复杂数据集的分析.
科学领域:
- 统计 统计 统计 统计
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 将分组数据与不可交换组进行聚类,会带来分析挑战.
- 现有的方法往往难以建模群体之间的复杂依赖关系.
研究的目的:
- 提出一种新的贝叶斯非参数方法,用于对依赖关系的分组数据进行集群.
- 为了使非可交换组之间能够共享集群,使用指向非循环图结构.
主要方法:
- 介绍了图形的迪里克莱特过程,贝叶斯的非参数模型.
- 利用定向非循环图来描述特定群体随机测量之间的依赖关系.
- 开发了一种有效的后置推理算法,用于模型估计.
主要成果:
- 图形的迪里克莱特过程联合模型依赖于特定组的随机测量.
- 该模型尊重导向非循环图的马尔科夫属性.
- 通过模拟和分析单细胞数据来证明模型的实用性.
结论:
- 图形的迪里克莱特过程提供了一个灵活的框架,用于集群复杂的分组数据.
- 该方法有效地处理不可交换的组及其依赖关系.
- 适用于各种领域,包括生物信息学和单细胞数据分析.
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