{"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/monte-carlo-dependency-estimation","title":"Monte Carlo Dependency Estimation","arxiv_id":"1810.02112","date":"2018-10-04","proceeding":null,"authors":["Edouard Fouché","Klemens Böhm"],"abstract":"Estimating the dependency of variables is a fundamental task in data\nanalysis. Identifying the relevant attributes in databases leads to better data\nunderstanding and also improves the performance of learning algorithms, both in\nterms of runtime and quality. In data streams, dependency monitoring provides\nkey insights into the underlying process, but is challenging. In this paper, we\npropose Monte Carlo Dependency Estimation (MCDE), a theoretical framework to\nestimate multivariate dependency in static and dynamic data. MCDE quantifies\ndependency as the average discrepancy between marginal and conditional\ndistributions via Monte Carlo simulations. Based on this framework, we present\nMann-Whitney P (MWP), a novel dependency estimator. We show that MWP satisfies\na number of desirable properties and can accommodate any kind of numerical\ndata. We demonstrate the superiority of our estimator by comparing it to the\nstate-of-the-art multivariate dependency measures.","url_abs":"http://arxiv.org/abs/1810.02112v1","url_pdf":"http://arxiv.org/pdf/1810.02112v1.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":"monte-carlo-dependency-estimation","repo_url":"https://github.com/edouardfouche/mcde","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"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}