具有随机参数的小数据模型的透度估计
Viacheslav Kovtun1, Torki Altameem2, Mohammed Al-Maitah2
1Department of Computer Control Systems, Vinnytsia National Technical University, Khmelnitske Shose Str., 95, Vinnytsia, 21000, Ukraine.
Heliyon
|February 1, 2024
概括
这项研究正式化了对线性和非线性小数据模型的最佳概率密度函数估计. 它通过最大限度地提高信息来应对有限,杂的测量挑战.
科学领域:
- 统计 统计 统计 统计
- 数学建模的数学建模
- 信息理论 信息理论
背景情况:
- 在数据集中,特别是小数据中,对依赖关系的正式化至关重要.
- 关于数据属性的假设是准确建模的关键.
- 现有的方法在有限和杂的小数据集上扎.
研究的目的:
- 在动态和静态小数据模型中对参数进行概率密度函数的最佳估计.
- 开发用于线性和非线性模型的方法,其中包含特定对象属性假设.
- 为了应对参数估计的挑战,使用有限的,被审查的和杂的测量.
主要方法:
- 概率理论和数学统计学.
- 信息理论和评估理论.
- 随机数学编程和信息最大化.
主要成果:
- 开发了一个基于最大化小数据信息的数学框架.
- 正式化的线性和非线性动态和静态小数据模型与随机参数.
- 成功确定了模型参数的概率密度函数的最佳估计.
结论:
- 正式化的程序为小型数据模型提供最佳参数估计.
- 该方法通过最大限度地提高信息,有效地处理受审查和噪音的测量.
- 优化问题可归结为随机线性编程的规范形式.
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