汇集概率分布和部分信息分解
1Department of Physics, University of Oregon, Eugene, Oregon 97403, USA.
Physical review. E
|June 17, 2023
概括
本研究通过定义协同,冗余和独特的信息来探索部分信息分解 (PID). 它提出了一种新的聚合方法,以解决对多个变量定义这些信息指标的模糊性.
科学领域:
- 信息理论 信息理论
- 多变量统计学 多变量统计学
- 计算神经科学是一种神经科学.
背景情况:
- 部分信息分解 (PID) 旨在在多个变量之间量化协同作用,冗余和独特的信息.
- 现有的PID框架缺乏对这些信息措施的准确定义的共识,导致模两可.
研究的目的:
- 为了说明PID中定义协同,冗余和独特信息的模两可的来源.
- 提出基于概率分布聚合的PID新框架.
主要方法:
- 将信息定义为概率分布之间的平均不确定性降低.
- 将协同效应信息解释为整体与其部分之和之间的差异,使用聚合的概率分布.
- 开发一个基于最佳概率分布聚合的格子结构.
主要成果:
- 拟议的聚合方法为PID引入了新的格子结构,与基于冗余的格子不同.
- 这个框架将概率分布与格子节点联系在一起,而不仅仅是平均.
- 概率分布之间的重叠成为描述协同和独特信息的关键量.
结论:
- 该研究提供了一种方法,通过利用概率分布聚合来解决部分信息分解中的模两可.
- 拟议的聚合方法提供了一种原则的方式来定义协同和独特的信息,提高对多变量信息的理解.
- 这项工作为分析多变量系统中复杂信息关系提供了灵活的框架.
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