{"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/elfi-engine-for-likelihood-free-inference","title":"ELFI: Engine for Likelihood-Free Inference","arxiv_id":"1708.00707","date":"2017-08-02","proceeding":null,"authors":["Jarno Lintusaari","Henri Vuollekoski","Antti Kangasrääsiö","Kusti Skytén","Marko Järvenpää","Pekka Marttinen","Michael U. Gutmann","Aki Vehtari","Jukka Corander","Samuel Kaski"],"abstract":"Engine for Likelihood-Free Inference (ELFI) is a Python software library for\nperforming likelihood-free inference (LFI). ELFI provides a convenient syntax\nfor arranging components in LFI, such as priors, simulators, summaries or\ndistances, to a network called ELFI graph. The components can be implemented in\na wide variety of languages. The stand-alone ELFI graph can be used with any of\nthe available inference methods without modifications. A central method\nimplemented in ELFI is Bayesian Optimization for Likelihood-Free Inference\n(BOLFI), which has recently been shown to accelerate likelihood-free inference\nup to several orders of magnitude by surrogate-modelling the distance. ELFI\nalso has an inbuilt support for output data storing for reuse and analysis, and\nsupports parallelization of computation from multiple cores up to a cluster\nenvironment. ELFI is designed to be extensible and provides interfaces for\nwidening its functionality. This makes the adding of new inference methods to\nELFI straightforward and automatically compatible with the inbuilt features.","url_abs":"http://arxiv.org/abs/1708.00707v3","url_pdf":"http://arxiv.org/pdf/1708.00707v3.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":"elfi-engine-for-likelihood-free-inference","repo_url":"https://github.com/elfi-dev/elfi","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"elfi-engine-for-likelihood-free-inference","repo_url":"https://github.com/hiit/elfi","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}