Acceptance-rejection (AR), Independent Metropolis Hastings (IMH) or importance sampling (IS) Monte Carlo (MC) simulation algorithms all involve computing ratios of probability density functions (pdfs). On the other hand, classifiers discriminate labellized samples produced by a mixture density model, i.e., a convex linear combination of two pdfs, and can thus be used for approximating the ratio of these two densities. This bridge between simulation and classification techniques enables us to propose (approximate) pdf-ratios-based simulation algorithms which are built only from a labellized training data set.

翻译：接受-拒绝（AR）、独立Metropolis Hastings（IMH）或重要性采样（IS）等蒙特卡洛（MC）模拟算法均涉及概率密度函数（pdfs）比值的计算。另一方面，分类器可区分由混合密度模型（即两个pdf的凸线性组合）生成的标记样本，因此能够用于近似这两个密度之间的比值。这种模拟与分类技术之间的桥梁使我们能够提出仅基于标记训练数据集构建的（近似）pdf比值模拟算法。