{"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/annealed-importance-sampling-with-q-paths","title":"Annealed Importance Sampling with q-Paths","arxiv_id":"2012.07823","date":"2020-12-14","proceeding":"NeurIPS Workshop DL-IG 2020 12","authors":["Rob Brekelmans","Vaden Masrani","Thang Bui","Frank Wood","Aram Galstyan","Greg Ver Steeg","Frank Nielsen"],"abstract":"Annealed importance sampling (AIS) is the gold standard for estimating partition functions or marginal likelihoods, corresponding to importance sampling over a path of distributions between a tractable base and an unnormalized target. 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