论文标题
有条件的蒙特卡洛重新审视
Conditional Monte Carlo revisited
论文作者
论文摘要
有条件的蒙特卡洛是指在函数t（x）的值t（x）= t的情况下从随机矢量x的条件分布中取样。设计了经典条件蒙特卡洛方法，用于通过从某些加权方案获得的无条件分布中取样来估算X功能的条件期望。基本成分是使用重要性采样和变量的变化。在本文中，我们通过引入人工参数模型来重新制定问题，该模型代表X给定t（x）= t的条件分布在此新模型中。关键是通过分布提供人工模型的参数。该方法通过几个示例说明了该方法，这些示例特别选择在此样本不直接的情况下说明条件抽样。还提供了模拟研究和对真实数据的拟合优度测试的应用。
Conditional Monte Carlo refers to sampling from the conditional distribution of a random vector X given the value T(X) = t for a function T(X). Classical conditional Monte Carlo methods were designed for estimating conditional expectations of functions of X by sampling from unconditional distributions obtained by certain weighting schemes. The basic ingredients were the use of importance sampling and change of variables. In the present paper we reformulate the problem by introducing an artificial parametric model, representing the conditional distribution of X given T(X)=t within this new model. The key is to provide the parameter of the artificial model by a distribution. The approach is illustrated by several examples, which are particularly chosen to illustrate conditional sampling in cases where such sampling is not straightforward. A simulation study and an application to goodness-of-fit testing of real data are also given.
