{"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/learning-discrete-bayesian-networks-from","title":"Learning Discrete Bayesian Networks from Continuous Data","arxiv_id":"1512.02406","date":"2015-12-08","proceeding":null,"authors":["Yi-Chun Chen","Tim Allan Wheeler","Mykel John Kochenderfer"],"abstract":"Learning Bayesian networks from raw data can help provide insights into the\nrelationships between variables. While real data often contains a mixture of\ndiscrete and continuous-valued variables, many Bayesian network structure\nlearning algorithms assume all random variables are discrete. Thus, continuous\nvariables are often discretized when learning a Bayesian network. However, the\nchoice of discretization policy has significant impact on the accuracy, speed,\nand interpretability of the resulting models. This paper introduces a\nprincipled Bayesian discretization method for continuous variables in Bayesian\nnetworks with quadratic complexity instead of the cubic complexity of other\nstandard techniques. Empirical demonstrations show that the proposed method is\nsuperior to the established minimum description length algorithm. In addition,\nthis paper shows how to incorporate existing methods into the structure\nlearning process to discretize all continuous variables and simultaneously\nlearn Bayesian network structures.","url_abs":"http://arxiv.org/abs/1512.02406v3","url_pdf":"http://arxiv.org/pdf/1512.02406v3.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":"learning-discrete-bayesian-networks-from","repo_url":"https://github.com/sisl/LearnDiscreteBayesNets.jl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}