Applying Monte Carlo simulates CIMC in the future thirty years yield.

应用蒙特卡罗法模拟了中集集团未来三十年的收益率。

This paper introduces generalized generation distribution factors and replaces the Monte Carlo simulation with extreme scenarios method,improving the calculation time and precision.

文中通过引入发电机功率分布因子,并用基于极限场景集的场景法取代基于蒙特卡洛仿真的场景法,在计算时间和计算精度2个方面进行了改善。

And the posterior distributions of parameters are obtained by Markov Chain Monte Carlo (MCMC) method.

应用马尔可夫链蒙特卡洛抽样方法求解参数验后估计值，给出了计算参数验后估计值的流程。

Compared with standard genetic algorithms and inverse Monte Carlo methods, it has been shown that the GGA has high searching efficient, and it can be rapidly obtained optimum results with a few generations.

与标准遗传算法和反演蒙特卡诺算法相比，该算法具有较高的搜索效率，能够用较少的迭代次数快速搜索到最优解。