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Conference Papers Year : 2016

Pliable rejection sampling

Akram Erraqabi
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Michal Valko
Odalric-Ambrym Maillard
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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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Dates and versions

hal-01322168 , version 1 (26-05-2016)

Identifiers

  • HAL Id : hal-01322168 , version 1

Cite

Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric-Ambrym Maillard. Pliable rejection sampling. International Conference on Machine Learning, Jun 2016, New York City, United States. ⟨hal-01322168⟩
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