{"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/probabilistic-inference-using-generators-the","title":"Probabilistic Inference Using Generators - The Statues Algorithm","arxiv_id":"1806.09997","date":"2018-06-24","proceeding":null,"authors":["Pierre Denis"],"abstract":"We present here a new probabilistic inference algorithm that gives exact\nresults in the domain of discrete probability distributions. This algorithm,\nnamed the Statues algorithm, calculates the marginal probability distribution\non probabilistic models defined as direct acyclic graphs. These models are made\nup of well-defined primitives that allow to express, in particular, joint\nprobability distributions, Bayesian networks, discrete Markov chains,\nconditioning and probabilistic arithmetic. The Statues algorithm relies on a\nvariable binding mechanism based on the generator construct, a special form of\ncoroutine; being related to the enumeration algorithm, this new algorithm\nbrings important improvements in terms of efficiency, which makes it valuable\nin regard to other exact marginalization algorithms. After introduction of\nseveral definitions, primitives and compositional rules, we present in details\nthe Statues algorithm. Then, we briefly discuss the interest of this algorithm\ncompared to others and we present possible extensions. Finally, we introduce\nLea and MicroLea, two Python libraries implementing the Statues algorithm,\nalong with several use cases. A proof of the correctness of the algorithm is\nprovided in appendix.","url_abs":"http://arxiv.org/abs/1806.09997v2","url_pdf":"http://arxiv.org/pdf/1806.09997v2.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":"probabilistic-inference-using-generators-the","repo_url":"https://bitbucket.org/piedenis/lea","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"probabilistic-inference-using-generators-the","repo_url":"https://bitbucket.org/piedenis/microlea","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}