论文标题
一些分布的有效依赖模型
Efficient dependency models for some distributions
论文作者
论文摘要
相关变量的依赖性函数与i）在存在因变量和/或相关变量的情况下执行不确定性定量和灵敏度分析，以及ii）模拟随机依赖变量。在本文中，我们在数学上得出了经典多元分布的实用依赖性函数，例如Dirichlet，椭圆形分布和独立的统一（分别是Gamma和Gaussian）变量，该变量在准备使用的约束下。由于此类依赖模型用于对随机值进行采样，并且我们为每个关节累积分布函数都有许多依赖模型，因此我们为使用多元敏感性分析提供了一种选择有效采样函数的方法。我们通过数值模拟说明了我们的方法。
Dependency functions of dependent variables are relevant for i) performing uncertainty quantification and sensitivity analysis in presence of dependent variables and/or correlated variables, and ii) simulating random dependent variables. In this paper, we mathematically derive practical dependency functions for classical multivariate distributions such as Dirichlet, elliptical distributions and independent uniform (resp. gamma and Gaussian) variables under constraints that are ready to be used. Since such dependency models are used for sampling random values and we have many dependency models for every joint cumulative distribution function, we provide a way for choosing the efficient sampling function using multivariate sensitivity analysis. We illustrate our approach by means of numerical simulations.
