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Adaptive rejection sampling using non-parametric estimators
Rejection Sampling is a basic Monte-Carlo method, used to sample from distributions admitting a probability density function, for which evaluations of the density are costly but still tractable. This technique is however rarely used as it stands, because of the high rejection rate it implies. Several methods have been explored to cope with this problem. Most of them either take advantage of strong assumptions on the form of the density, or do not present any performance guarantee. We aim at studying the minimax properties of this problem over the class of smooth density functions


non-parametric estimators

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