随机变量正逆变换的几个特征

P. Kosobutskyy, М. V. Ferens
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引用次数: 0

摘要

在复杂系统中获得和处理实验结果的实际任务。随机障碍(误差)、测量误差、数学模型和数据处理算法的不完善和限制可以改变分布的外观,并导致算法的不正确使用,例如控制系统中的卡尔曼滤波。识别分布规律的复杂方法需要研究量子系统、自然现象、环境、生物等过程,这些过程以分布的奇点和多模态的存在为特征。因此,通常不建议采用单独的分布规律来模拟概率性实验数据分布,而是将广义分布作为一个单一的统计系统,其中已知的分布包括作为个别的局部情况。因此,广义伽玛分布包括Rayleigh、Maxwell、Weibull、Levy、Hi-Square分布,广泛应用于物理过程研究、遥感、可靠性理论等统计方法相关的应用问题中,用于描述颗粒破碎的分散组成以及气液流相分离效率的计算。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Some Features of the Direct and Inverse Transformation of Random Variables
Always actual tasks of obtaining and processing experimental results in complex systems. Random obstacles (errors), measurement errors, imperfections and limitations of mathematical models and data processing algorithms can change the appearance of the distribution and lead to incorrect use of algorithms, for example, as is the case with Kalman filtering in control systems. Complex methods for the identification of distribution laws require the study of quantum systems, natural phenomena, environmental, biological, etc. processes, which are characterized by the presence of singularities and multimodality of distributions. Therefore, it is often not recommended to apply separate distribution laws to simulate probabilistic experimental data distributions, but a generalized distribution as a single statistical system, which known distributions include as individual partial cases. Thus, the generalized gamma distribution includes Rayleigh, Maxwell, Weibull, Levy, Hi-Square distributions, which are widely used in applied problems associated with statistical methods of physical processes research, remote sensing, in the theory of reliability, for describing the dispersion composition of particles fragmentation and calculation of the efficiency of phase separation in gas-liquid streams.
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