# A minimax near-optimal algorithm for adaptive rejection sampling

@article{Achdou2018AMN, title={A minimax near-optimal algorithm for adaptive rejection sampling}, author={Juliette Achdou and Joseph C. Lam and Alexandra Carpentier and Gilles Blanchard}, journal={ArXiv}, year={2018}, volume={abs/1810.09390} }

Rejection Sampling is a fundamental Monte-Carlo method. It is used to sample from distributions admitting a probability density function which can be evaluated exactly at any given point, albeit at a high computational cost. However, without proper tuning, this technique implies a high rejection rate. Several methods have been explored to cope with this problem, based on the principle of adaptively estimating the density by a simpler function, using the information of the previous samples. Most…

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