低概率状态,数据统计和估计
Damián G Hernández1,2, Ahmed Roman1, Ilya Nemenman1,3,4
1Department of Physics, Emory University, Atlanta, Georgia, USA.
Physical review. E
|August 16, 2023
概括
在复杂系统中估计是具有挑战性的,因为未采样状态. 这项研究揭示了关键数据统计数据,如样本大小和巧合,这些数据塑造了低样本分布的贝叶斯 Entropy 估计器.
科学领域:
- 复杂系统分析 复杂系统分析
- 信息理论 信息理论
- 统计建模 统计建模
背景情况:
- 在复杂系统中估计概率分布的是至关重要的,但很困难.
- 最大概率估计器被未采样状态所偏见,低估了真实.
- 贝叶斯估计器通过建模低概率尾巴来解决这个问题,但驱动因素仍然不清楚.
研究的目的:
- 确定观察到的数据的统计特征,这些特征决定了贝叶斯 entropy 估计器中的尾部模型.
- 为低样本分布开发近似的分析估计器.
- 为了提供一个直观的理解贝叶斯 entropy 估计器是如何工作的.
主要方法:
- 对离散概率分布的众所周知的估计器的分析.
- 基于已识别的数据统计数据的近似分析估计器的推导.
- 调查样本大小的影响,最大概率估计和巧合统计.
主要成果:
- 影响尾部建模的关键数据统计包括样本大小,最大概率估计,巧合数和巧合分散.
- 对于低样本分布,获得了近似的分析估计器.
- 这项研究阐明了数据统计和贝叶斯 Entropy 估计之间的关系.
结论:
- 贝叶斯 Entropy 估计中的低概率尾巴的结构主要由几个基本数据统计统计控制.
- 衍生出的分析估计器为不足样本的场景提供了一种实际的方法.
- 这项工作提高了复杂系统分析中贝叶斯值估计的解释性.
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