{"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-bayesian-approximation-method-for-online","title":"A Bayesian Approximation Method for Online Ranking","arxiv_id":null,"date":"2011-01-01","proceeding":"Journal of Machine Learning Research 2011 1","authors":["Ruby C. Weng","Chih-Jen Lin"],"abstract":"This paper describes a Bayesian approximation method to obtain online ranking algorithms for\r\ngames with multiple teams and multiple players. Recently for Internet games large online ranking\r\nsystems are much needed. We consider game models in which a k-team game is treated as several\r\ntwo-team games. By approximating the expectation of teams’ (or players’) performances, we derive\r\nsimple analytic update rules. These update rules, without numerical integrations, are very easy to\r\ninterpret and implement. Experiments on game data show that the accuracy of our approach is\r\ncompetitive with state of the art systems such as TrueSkill, but the running time as well as the code\r\nis much shorter.","url_abs":"https://jmlr.org/papers/v12/weng11a.html","url_pdf":"https://jmlr.org/papers/volume12/weng11a/weng11a.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-bayesian-approximation-method-for-online","repo_url":"https://github.com/OpenDebates/openskill.py","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"a-bayesian-approximation-method-for-online","repo_url":"https://github.com/Vaschex/openskill.lua","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"a-bayesian-approximation-method-for-online","repo_url":"https://github.com/brezinajn/openskill.kt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"a-bayesian-approximation-method-for-online","repo_url":"https://github.com/philihp/openskill.js","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}