{"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/an-expectation-conditional-maximization","title":"An Expectation Conditional Maximization approach for Gaussian graphical models","arxiv_id":"1709.06970","date":"2017-09-20","proceeding":null,"authors":["Zehang Richard Li","Tyler H. McCormick"],"abstract":"Bayesian graphical models are a useful tool for understanding dependence\nrelationships among many variables, particularly in situations with external\nprior information. In high-dimensional settings, the space of possible graphs\nbecomes enormous, rendering even state-of-the-art Bayesian stochastic search\ncomputationally infeasible. We propose a deterministic alternative to estimate\nGaussian and Gaussian copula graphical models using an Expectation Conditional\nMaximization (ECM) algorithm, extending the EM approach from Bayesian variable\nselection to graphical model estimation. We show that the ECM approach enables\nfast posterior exploration under a sequence of mixture priors, and can\nincorporate multiple sources of information.","url_abs":"http://arxiv.org/abs/1709.06970v3","url_pdf":"http://arxiv.org/pdf/1709.06970v3.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":"an-expectation-conditional-maximization","repo_url":"https://github.com/richardli/EMGS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"variable-selection","task_name":"Variable Selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}