Pliable rejection sampling

TL;DR

Pliable rejection sampling (PRS) introduces a kernel estimator-based approach to improve sampling efficiency and provide guarantees on accepted sample counts for difficult distributions.

Contribution

PRS is a novel rejection sampling method that learns proposals with kernel estimators, offering performance guarantees and broad applicability.

Findings

Samples are with high probability i.i.d. and follow distribution f.

PRS provides a guarantee on the number of accepted samples.

Method improves efficiency over traditional rejection sampling.

Abstract

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.