Partial Rejection Sampling is an algorithmic approach to obtaining a perfect sample from a specified distribution. The objects to be sampled are assumed to be represented by a number of random variables. In contrast to classical rejection sampling, in which all variables are resampled until a feasible solution is found, partial rejection sampling aims at greater efficiency by resampling only a subset of variables that `go wrong'. Partial rejection sampling is closely related to Moser and Tardos' algorithmic version of the Lov\'asz Local Lemma, but with the additional requirement that a specified output distribution should be met. This article provides a largely self-contained account of the basic form of the algorithm and its analysis.

翻译：部分拒绝采样是一种从指定分布中获取完美样本的算法方法。待采样的对象被假定为由若干随机变量表示。与经典拒绝采样中所有变量被重新采样直到找到可行解不同，部分拒绝采样通过仅重新采样“出错”的变量子集来提高效率。部分拒绝采样与Moser和Tardos提出的Lovász局部引理的算法版本密切相关，但额外要求满足指定的输出分布。本文主要独立地阐述了该算法的基本形式及其分析。