提出可自适应调整的拒绝采样方法，提升采样效率且有理论保证。
Pliable rejection sampling
- 用核估计器学习采样提议分布，动态优化采样效率。
- 保证接受样本数量，显著降低传统拒绝采样的高拒绝率问题。
- 适用于复杂分布采样，适合需要严格理论保障的研究者。
拒绝采样是针对复杂分布采样的经典方法，但受限于高拒绝率。现有自适应方法多仅适用于特定分布或缺乏性能保证。本文提出可塑性拒绝采样（Pliable Rejection Sampling, PRS），利用核估计器学习采样提议分布。由于基于经典拒绝采样框架，所得样本以高概率独立同分布且服从目标分布 f。此外，PRS提供对接受样本数量的理论保证，有效提升采样效率。
原文摘要 · Abstract (English)
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.
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