使用运输地图学习的累积分布函数的近似值
Dawen Wu1,2, Ludovic Chamoin3
1CNRS@CREATE, 1 Create Way, #08-01 Create Tower, Singapore 138602, Singapore.
Chaos (Woodbury, N.Y.)
|September 19, 2025
概括
本研究介绍了运输地图学习 (TML) 以准确,数据效率高的累积分布函数 (CDF) 的近似值. 与传统方法相比,TML提供了更高的性能,尤其是在数据有限的情况下.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 数字分析 数字分析
背景情况:
- 对于许多概率分布,累积分布函数 (CDF) 缺乏基本的闭式表达式.
- 现有的近似方法,如实证CDF,通常需要大量的样本数据以获得准确性.
研究的目的:
- 为CDF开发准确且数据效率高的闭式近似.
- 为了解决当前CDF近似技术的局限性.
主要方法:
- 灵感来自运输地图理论,特别是运输地图等于CDF的单维情况.
- 拟议的运输地图学习 (TML) 使用经过训练以接近CDF的神经网络.
- 神经网络的输出通过sigmoid函数传递,以确保[0,1]范围.
主要成果:
- TML在标准正常,β和马分布的近似CDF中表现出高精度.
- 与实证CDF方法与插值相比,获得了更高的准确性.
- 通过基准概率分布验证的有效性.
结论:
- 运输地图学习 (TML) 为封闭形式的CDF近似提供了一个强大的,数据效率高的方法.
- 该方法比现有技术有了显著的改进,特别是在数据稀缺的情况下.
- TML 是一个有前途的工具,用于统计建模和分析,其中CDF是关键的.
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