几何分区:一个连续状态空间的粗粒度
Christopher Tyler Diggans1, Abd AlRahman R AlMomani2
1Information Directorate, Air Force Research Laboratory, Rome, NY 13441, USA.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
这项研究引入了一种新的几何分区来量化连续数据中的无知. 这项新措施为复杂数据集提供了比传统方法更一致和更有信息的方法.
科学领域:
- 信息理论 信息理论
- 统计力学 统计力学
- 时间序列分析时间序列分析
背景情况:
- 传统的估计器 (热力学,香农) 对于连续数据是离散的和有问题的.
- 不同的定义面临的局限性类似于热力学挑战.
- 采样数据代表微态,而底层的宏态通常是未知的.
研究的目的:
- 重新检查作为无知的一种措施在连续现象可预测性.
- 开发一种适合采样,连续数据的新型估计器.
- 解决现有的离散和连续测量的局限性.
主要方法:
- 使用数据样本量值定义的宏观状态.
- 开发了一个基于量子距离的无知密度分布.
- 计算了几何分区作为这个分布的香农.
主要成果:
- 几何分区是比直方图-binning更一致和有信息的.
- 这种方法在复杂的分布,异常值和有限的抽样方面表现出色.
- 它在计算上是高效的,并避免负值,优于k-最近邻居估计器.
结论:
- 几何分区为连续现象的无知提供了强大的量化.
- 该方法适用于时间序列分析和近似 ergodic 符号动态.
- 这种估计器在处理现实世界,有限的观测数据方面提供了独特的优势.
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