{"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/robust-bayesian-model-selection-for-variable","title":"Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical Model","arxiv_id":"1806.05924","date":"2018-06-15","proceeding":null,"authors":["Daniel Andrade","Akiko Takeda","Kenji Fukumizu"],"abstract":"Variable clustering is important for explanatory analysis. However, only few\ndedicated methods for variable clustering with the Gaussian graphical model\nhave been proposed. Even more severe, small insignificant partial correlations\ndue to noise can dramatically change the clustering result when evaluating for\nexample with the Bayesian Information Criteria (BIC). In this work, we try to\naddress this issue by proposing a Bayesian model that accounts for negligible\nsmall, but not necessarily zero, partial correlations. Based on our model, we\npropose to evaluate a variable clustering result using the marginal likelihood.\nTo address the intractable calculation of the marginal likelihood, we propose\ntwo solutions: one based on a variational approximation, and another based on\nMCMC. Experiments on simulated data shows that the proposed method is similarly\naccurate as BIC in the no noise setting, but considerably more accurate when\nthere are noisy partial correlations. Furthermore, on real data the proposed\nmethod provides clustering results that are intuitively sensible, which is not\nalways the case when using BIC or its extensions.","url_abs":"http://arxiv.org/abs/1806.05924v1","url_pdf":"http://arxiv.org/pdf/1806.05924v1.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":"robust-bayesian-model-selection-for-variable","repo_url":"https://github.com/andrade-stats/robustBayesClustering","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"model-selection","task_name":"Model Selection"},{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}