Pliable Rejection Sampling

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Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric Maillard ;
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2121-2129, 2016.

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.

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