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 labeled samples produced by a mixture of two distributions and can be used for approximating the ratio of the two corresponding pdfs.This bridge between simulation and classification enables us to propose pdf-free versions of pdf-ratio-based simulation algorithms, where the ratio is replaced by a surrogate function computed via a classifier. From a probabilistic modeling perspective, our procedure involves a structured energy based model which can easily be trained and is compatible with the classical samplers.

翻译：接受-拒绝（AR）、独立Metropolis Hastings（IMH）或重要性采样（IS）等蒙特卡罗（MC）模拟算法均涉及概率密度函数（pdfs）比值的计算。另一方面，分类器可对由两个分布混合产生的标记样本进行判别，并用于近似对应两概率密度函数的比值。这种模拟与分类之间的桥梁使我们能够提出基于pdf比值的模拟算法的无pdf版本，其中比值被通过分类器计算的替代函数所取代。从概率建模的角度来看，我们的方法涉及一种结构化能量基模型，该模型易于训练且与经典采样器兼容。