通过量化实现的决策来分解和追踪相互信息 地区
Tobias Mages1, Christian Rohner1
1Department of Information Technology, Uppsala University, 752 36 Uppsala, Sweden.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
本研究引入了一种新的部分信息分解 (PID) 测量方法,它满足关键公理,并提供非负信息组件. 新方法准确地从多个变量中量化独特,冗余和协同的信息.
科学领域:
- 信息理论 信息理论
- 机器学习 机器学习
- 统计推理 统计推理
背景情况:
- 部分信息分解 (PID) 旨在将来自多个变量的相互信息组件归因于目标.
- 现有的PID措施因未能满足公理或产生负面信息组件而受到批评.
- 需要一个强大的PID测量方法,它遵循理论原则,并提供非负的分解.
研究的目的:
- 开发一种用于部分信息分解 (PID) 的新方法.
- 确保新措施满足所需的公理,包括包含-排除原则.
- 为任意数量的变量提供非负的信息分解.
主要方法:
- 解释可实现的I/II类错误对作为目标变量预测的点性不确定性.
- 构建一个分布格子,以相互信息作为一致的估值.
- 基于可达到的决策区域,为构造的测量开发一个代数.
主要成果:
- 拟议的PID措施满足了最初的公理和包含-排除原则.
- 分解为任意数量的变量产生非负元件.
- 通过马尔科夫链来追踪信息流的实际应用.
结论:
- 开发的PID测量提供了一个理论上可靠且实际适用的信息分解方法.
- 这种方法可以用于分析复杂系统中的信息流,例如通信网络和数据处理.
- 非负分解为信息共享和协同提供了更直观的理解.
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