在水利水电工程的风险分析中，组合分布模型的提出将有重要的理论意义和实用价值。
Proffering the combined distribution model will be valuable to theory and application in the risk analysis of the hydraulic engineering.
建立在极端事件风险的理论基础上，提出了由原始分布和尾分布组成的组合分布模型。
Based on the theory of extreme event risk, a combination distribution model composed of the original distribution and tail distribution was put forward.
本文提出了适用于我国沿海风暴潮影响地区的增减水重现值计算模型——组合分布模型。
This paper also presents a Combined Distribution Model, which is suitable for storm surge frequency analysis.
本文总结了海岸工程水文的一维统计分布模型，提出了适用于我国沿海风暴潮影响地区的增减水重现值计算模型——组合分布模型。
To summarize the univariate extreme value distribution models and put forward a Combined distribution model, which is suitable for statistical analysis of storm surge in typhoon-effected area.
提出了适用于大规模网络的组合随机用户平衡模型，能够同时预测出行分布和交通分配。
A combined stochastic user equilibrium model for simultaneous prediction of trip distribution and trip assignment on large-scale networks is developed.
文章分析了“0 - 1”分布模型的使用规律，并对两个典型问题提出了其他的组合解法。
This paper analyzes the application rule of the model of "0-1" distribution and proposes other combined solutions to the two typical problems.
在传统的金融风险度量模型中，基本都是基于正态分布，然后运用方差一协方差法来求解资产组合的风险价值。
In the traditional financial risk measurement model, the basic method is based on normal distribution, and then the variance-covariance method used to solve the portfolio value at risk.
与传统模型相比，本文所提出的基于随机分布函数的生育率组合模型和死亡率分段模型使模型精度得到了进一步的提高。
Compared with traditional functions, both the composite fertility model which is based on random distribution functions and the segmented mortality model improved the models 'accuracy.
信用资产组合模型的分布计算是信用计量模型中一个非常重要的主题，对于这个问题一般常用的方法是进行蒙特卡罗随机模拟。
A new bilevel programming model-bilevel stochastic programming model is presented and the genetic algorithms based on Monte Carlo simulation to solve bilevel stochastic programming problem is given.
信用资产组合模型的分布计算是信用计量模型中一个非常重要的主题，对于这个问题一般常用的方法是进行蒙特卡罗随机模拟。
A new bilevel programming model-bilevel stochastic programming model is presented and the genetic algorithms based on Monte Carlo simulation to solve bilevel stochastic programming problem is given.
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