{"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-large-scale-bayesian-networks-with","title":"Learning Large-Scale Bayesian Networks with the sparsebn Package","arxiv_id":"1703.04025","date":"2017-03-11","proceeding":null,"authors":["Bryon Aragam","Jiaying Gu","Qing Zhou"],"abstract":"Learning graphical models from data is an important problem with wide\napplications, ranging from genomics to the social sciences. Nowadays datasets\noften have upwards of thousands---sometimes tens or hundreds of thousands---of\nvariables and far fewer samples. To meet this challenge, we have developed a\nnew R package called sparsebn for learning the structure of large, sparse\ngraphical models with a focus on Bayesian networks. While there are many\nexisting software packages for this task, this package focuses on the unique\nsetting of learning large networks from high-dimensional data, possibly with\ninterventions. As such, the methods provided place a premium on scalability and\nconsistency in a high-dimensional setting. Furthermore, in the presence of\ninterventions, the methods implemented here achieve the goal of learning a\ncausal network from data. Additionally, the sparsebn package is fully\ncompatible with existing software packages for network analysis.","url_abs":"http://arxiv.org/abs/1703.04025v2","url_pdf":"http://arxiv.org/pdf/1703.04025v2.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-large-scale-bayesian-networks-with","repo_url":"https://github.com/itsrainingdata/sparsebn-reproduce","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"learning-large-scale-bayesian-networks-with","repo_url":"https://github.com/itsrainingdata/sparsebn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"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}