{"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/optimized-realization-of-bayesian-networks-in","title":"Optimized Realization of Bayesian Networks in Reduced Normal Form using Latent Variable Model","arxiv_id":"1901.06201","date":"2019-01-18","proceeding":null,"authors":["Giovanni Di Gennaro","Amedeo Buonanno","Francesco A. N. Palmieri"],"abstract":"Bayesian networks in their Factor Graph Reduced Normal Form (FGrn) are a\npowerful paradigm for implementing inference graphs. Unfortunately, the\ncomputational and memory costs of these networks may be considerable, even for\nrelatively small networks, and this is one of the main reasons why these\nstructures have often been underused in practice. In this work, through a\ndetailed algorithmic and structural analysis, various solutions for cost\nreduction are proposed. An online version of the classic batch learning\nalgorithm is also analyzed, showing very similar results (in an unsupervised\ncontext); which is essential even if multilevel structures are to be built. The\nsolutions proposed, together with the possible online learning algorithm, are\nincluded in a C++ library that is quite efficient, especially if compared to\nthe direct use of the well-known sum-product and Maximum Likelihood (ML)\nalgorithms. The results are discussed with particular reference to a Latent\nVariable Model (LVM) structure.","url_abs":"http://arxiv.org/abs/1901.06201v1","url_pdf":"http://arxiv.org/pdf/1901.06201v1.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":"optimized-realization-of-bayesian-networks-in","repo_url":"https://github.com/mlunicampania/FGrnLib","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"form","task_name":"Form"}],"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}