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Researchers introduce pliable rejection sampling using kernel estimators

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced pliable rejection sampling (PRS), a novel method designed to improve the efficiency of sampling from complex probability distributions. Traditional rejection sampling methods often suffer from high rejection rates, limiting their practical use. PRS addresses this by employing a kernel estimator to learn the sampling proposal, offering a performance guarantee on the number of accepted samples while ensuring the generated samples are i.i.d. and correctly distributed. AI

IMPACT Introduces a new statistical technique that could improve the efficiency of sampling in machine learning models.

RANK_REASON The cluster describes a new academic paper detailing a novel statistical sampling method.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Researchers introduce pliable rejection sampling using kernel estimators

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The cluster describes a new academic paper detailing a novel statistical sampling method.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric-Ambrym Maillard ·

    Pliable rejection sampling

    arXiv:2604.22385v1 Announce Type: new 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 wi…

  2. arXiv stat.ML TIER_1 English(EN) · Odalric-Ambrym Maillard ·

    Pliable rejection sampling

    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 …

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