{"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/approximate-probabilistic-inference-via-word","title":"Approximate Probabilistic Inference via Word-Level Counting","arxiv_id":"1511.07663","date":"2015-11-24","proceeding":null,"authors":["Supratik Chakraborty","Kuldeep S. Meel","Rakesh Mistry","Moshe Y. Vardi"],"abstract":"Hashing-based model counting has emerged as a promising approach for\nlarge-scale probabilistic inference on graphical models. A key component of\nthese techniques is the use of xor-based 2-universal hash functions that\noperate over Boolean domains. Many counting problems arising in probabilistic\ninference are, however, naturally encoded over finite discrete domains.\nTechniques based on bit-level (or Boolean) hash functions require these\nproblems to be propositionalized, making it impossible to leverage the\nremarkable progress made in SMT (Satisfiability Modulo Theory) solvers that can\nreason directly over words (or bit-vectors). In this work, we present the first\napproximate model counter that uses word-level hashing functions, and can\ndirectly leverage the power of sophisticated SMT solvers. Empirical evaluation\nover an extensive suite of benchmarks demonstrates the promise of the approach.","url_abs":"http://arxiv.org/abs/1511.07663v3","url_pdf":"http://arxiv.org/pdf/1511.07663v3.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":"approximate-probabilistic-inference-via-word","repo_url":"https://bitbucket.org/kuldeepmeel/smtapproxmc","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":"https://app.syntology.ai/?focus=1511.07663","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}