{"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/minimal-i-map-mcmc-for-scalable-structure","title":"Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models","arxiv_id":"1803.05554","date":"2018-03-15","proceeding":"ICML 2018 7","authors":["Raj Agrawal","Tamara Broderick","Caroline Uhler"],"abstract":"Learning a Bayesian network (BN) from data can be useful for decision-making\nor discovering causal relationships. However, traditional methods often fail in\nmodern applications, which exhibit a larger number of observed variables than\ndata points. The resulting uncertainty about the underlying network as well as\nthe desire to incorporate prior information recommend a Bayesian approach to\nlearning the BN, but the highly combinatorial structure of BNs poses a striking\nchallenge for inference. The current state-of-the-art methods such as order\nMCMC are faster than previous methods but prevent the use of many natural\nstructural priors and still have running time exponential in the maximum\nindegree of the true directed acyclic graph (DAG) of the BN. We here propose an\nalternative posterior approximation based on the observation that, if we\nincorporate empirical conditional independence tests, we can focus on a\nhigh-probability DAG associated with each order of the vertices. We show that\nour method allows the desired flexibility in prior specification, removes\ntiming dependence on the maximum indegree and yields provably good posterior\napproximations; in addition, we show that it achieves superior accuracy,\nscalability, and sampler mixing on several datasets.","url_abs":"http://arxiv.org/abs/1803.05554v3","url_pdf":"http://arxiv.org/pdf/1803.05554v3.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":"minimal-i-map-mcmc-for-scalable-structure","repo_url":"https://github.com/miraep8/Minimal_IMAP_MCMC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.05554","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}