ICML 2016
Pliable 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.
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Context
- Venue
- International Conference on Machine Learning
- Archive span
- 1993-2025
- Indexed papers
- 16471
- Paper id
- 859759291252101226