Rejection sampling is a technique for sampling from difficult distributions. However, its use is limited due to a high rejection rate. Common adaptive rejection sampling methods either work only for very specific distributions or without performance guarantees. In this paper, we present pliable rejection sampling (PRS), a new approach to rejection sampling, where we learn the sampling proposal using a kernel estimator. Since our method builds on rejection sampling, the samples obtained are with high probability i.i.d. and distributed according to f. Moreover, PRS comes with a guarantee on the number of accepted samples.

翻译：拒收抽样是一种从复杂分布中抽样的技术。然而，由于高拒绝率，其应用受到限制。现有的自适应拒收抽样方法要么仅适用于特定分布，要么缺乏性能保证。本文提出可塑性拒收抽样（Pliable Rejection Sampling, PRS），一种基于核估计器学习抽样提议的新方法。由于该方法建立在拒收抽样的基础上，所得样本以高概率独立同分布，且服从分布f。此外，PRS对接受样本的数量具有保证。