基于信息几何学的多变量高斯系统的部分信息分解
1School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8QQ, UK.
Entropy (Basel, Switzerland)
|July 26, 2024
概括
这项研究将部分信息分解扩展到多变量高斯系统,提供明确的公式和验证理论性质. 新的算法显示出有希望的结果,但有时可能错误地估计了协同作用和独特的信息水平.
科学领域:
- 信息理论 信息理论
- 统计建模 统计建模
- 机器学习 机器学习
背景情况:
- 部分信息分解 (PID) 对于理解复杂系统至关重要.
- 现有的算法,如Niu and Quinn (2019) 的算法,仅限于更简单的案例 (例如,三个标量变量).
- 信息几何学为分析概率分布提供了一个框架.
研究的目的:
- 将部分信息分解概括为具有矢量输入和输出的多变量高斯系统.
- 导出这些复杂系统中PID组件的明确数学表达式.
- 为了验证拟议的分解方法的理论性质.
主要方法:
- 杆化标准来自信息几何学的结果.
- 在多变量高斯系统中为PID组件衍生了显式表达式.
- 使用受约束的凸凸优化来确定一个关键参数.
主要成果:
- 在多变量高斯系统中成功衍生了PID组件的明确公式.
- 拟议的分解方法满足了关键的理论性质 (非负性,对称性等). ) 的情况.
- 经验应用显示,在某些情况下,可能高估了协同作用/共享信息,低估了独特信息.
结论:
- 一般化的PID算法适用于复杂的多变量高斯系统.
- 该方法提供了有价值的见解,但由于潜在的信息组件误估,需要仔细解释.
- 与现有方法 (Idep,Immi) 的比较显示了结果的相似性和明显差异.
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