Pliable rejection sampling
Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric-Ambrym Maillard
TLDR
Pliable Rejection Sampling (PRS) learns proposals via kernel estimation, providing i.i.d. samples with high probability and guaranteed acceptance rates.
Key contributions
- Addresses the high rejection rate limitation of traditional rejection sampling.
- Introduces Pliable Rejection Sampling (PRS) that learns proposals via a kernel estimator.
- Ensures high probability i.i.d. samples from the target distribution f.
- Offers a performance guarantee on the number of accepted samples.
Why it matters
This paper significantly improves rejection sampling, a fundamental technique for difficult distributions, by addressing its high rejection rate. It offers a robust method with performance guarantees, overcoming limitations of prior adaptive approaches. This makes sampling from complex distributions more efficient and reliable.
Original 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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