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.

翻译：有条件的蒙特卡洛是指根据T(X) = T(X) = t 函数函数T(X)的值从随机矢量X的有条件分布中取样。典型的有条件的蒙特卡洛方法设计为通过某些加权办法获得的无条件分布的抽样估计对X功能的有条件预期值。基本要素是使用重要取样和变数的变化。在本文件中,我们通过采用人工参数模型来重新表述问题,代表了X在这一新模型中的有条件分布。关键在于通过分布提供人造模型的参数。方法由几个例子加以说明,这些例子特别用来说明在这种抽样不简单的情况下有条件取样的情况。还进行了模拟研究,并应用对真实数据进行良好的测试。