{"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/exact-structure-learning-of-bayesian-networks","title":"Exact Structure Learning of Bayesian Networks by Optimal Path Extension","arxiv_id":"1608.02682","date":"2016-08-09","proceeding":null,"authors":["Subhadeep Karan","Jaroslaw Zola"],"abstract":"Bayesian networks are probabilistic graphical models often used in big data\nanalytics. The problem of exact structure learning is to find a network\nstructure that is optimal under certain scoring criteria. The problem is known\nto be NP-hard and the existing methods are both computationally and memory\nintensive. In this paper, we introduce a new approach for exact structure\nlearning. Our strategy is to leverage relationship between a partial network\nstructure and the remaining variables to constraint the number of ways in which\nthe partial network can be optimally extended. Via experimental results, we\nshow that the method provides up to three times improvement in runtime, and\norders of magnitude reduction in memory consumption over the current best\nalgorithms.","url_abs":"http://arxiv.org/abs/1608.02682v3","url_pdf":"http://arxiv.org/pdf/1608.02682v3.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":"exact-structure-learning-of-bayesian-networks","repo_url":"https://gitlab.com/SCoRe-Group/SABNA-Release","is_official":1,"mentioned_in_paper":0,"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}