{"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/integrating-additional-knowledge-into","title":"Integrating Additional Knowledge Into Estimation of Graphical Models","arxiv_id":"1704.02739","date":"2017-04-10","proceeding":null,"authors":["Yunqi Bu","Johannes Lederer"],"abstract":"In applications of graphical models, we typically have more information than\njust the samples themselves. A prime example is the estimation of brain\nconnectivity networks based on fMRI data, where in addition to the samples\nthemselves, the spatial positions of the measurements are readily available.\nWith particular regard for this application, we are thus interested in ways to\nincorporate additional knowledge most effectively into graph estimation. Our\napproach to this is to make neighborhood selection receptive to additional\nknowledge by strengthening the role of the tuning parameters. We demonstrate\nthat this concept (i) can improve reproducibility, (ii) is computationally\nconvenient and efficient, and (iii) carries a lucid Bayesian interpretation. We\nspecifically show that the approach provides effective estimations of brain\nconnectivity graphs from fMRI data. However, providing a general scheme for the\ninclusion of additional knowledge, our concept is expected to have applications\nin a wide range of domains.","url_abs":"http://arxiv.org/abs/1704.02739v2","url_pdf":"http://arxiv.org/pdf/1704.02739v2.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":"integrating-additional-knowledge-into","repo_url":"https://github.com/LedererLab/GGM-FDR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"integrating-additional-knowledge-into","repo_url":"https://github.com/LedererLab/fMRI","is_official":0,"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}