{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-minimax-near-optimal-algorithm-for-adaptive","title":"A minimax near-optimal algorithm for adaptive rejection sampling","arxiv_id":"1810.09390","date":"2018-10-22","proceeding":null,"authors":["Juliette Achdou","Joseph C. Lam","Alexandra Carpentier","Gilles Blanchard"],"abstract":"Rejection Sampling is a fundamental Monte-Carlo method. It is used to sample\nfrom distributions admitting a probability density function which can be\nevaluated exactly at any given point, albeit at a high computational cost.\nHowever, without proper tuning, this technique implies a high rejection rate.\nSeveral methods have been explored to cope with this problem, based on the\nprinciple of adaptively estimating the density by a simpler function, using the\ninformation of the previous samples. Most of them either rely on strong\nassumptions on the form of the density, or do not offer any theoretical\nperformance guarantee. We give the first theoretical lower bound for the\nproblem of adaptive rejection sampling and introduce a new algorithm which\nguarantees a near-optimal rejection rate in a minimax sense.","url_abs":"http://arxiv.org/abs/1810.09390v1","url_pdf":"http://arxiv.org/pdf/1810.09390v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-minimax-near-optimal-algorithm-for-adaptive","repo_url":"https://github.com/josephclam/NNARS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}