{"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/block-value-symmetries-in-probabilistic","title":"Block-Value Symmetries in Probabilistic Graphical Models","arxiv_id":"1807.00643","date":"2018-07-02","proceeding":null,"authors":["Gagan Madan","Ankit Anand","Mausam","Parag Singla"],"abstract":"One popular way for lifted inference in probabilistic graphical models is to\nfirst merge symmetric states into a single cluster (orbit) and then use these\nfor downstream inference, via variations of orbital MCMC [Niepert, 2012]. These\norbits are represented compactly using permutations over variables, and\nvariable-value (VV) pairs, but they can miss several state symmetries in a\ndomain.\n  We define the notion of permutations over block-value (BV) pairs, where a\nblock is a set of variables. BV strictly generalizes VV symmetries, and can\ncompute many more symmetries for increasing block sizes. To operationalize use\nof BV permutations in lifted inference, we describe 1) an algorithm to compute\nBV permutations given a block partition of the variables, 2) BV-MCMC, an\nextension of orbital MCMC that can sample from BV orbits, and 3) a heuristic to\nsuggest good block partitions. Our experiments show that BV-MCMC can mix much\nfaster compared to vanilla MCMC and orbital MCMC.","url_abs":"http://arxiv.org/abs/1807.00643v2","url_pdf":"http://arxiv.org/pdf/1807.00643v2.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":"block-value-symmetries-in-probabilistic","repo_url":"https://github.com/dair-iitd/bv-mcmc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}