{"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/bayesian-optimization-for-probabilistic","title":"Bayesian Optimization for Probabilistic Programs","arxiv_id":"1707.04314","date":"2017-07-13","proceeding":"NeurIPS 2016 12","authors":["Tom Rainforth","Tuan Anh Le","Jan-Willem van de Meent","Michael A. Osborne","Frank Wood"],"abstract":"We present the first general purpose framework for marginal maximum a\nposteriori estimation of probabilistic program variables. By using a series of\ncode transformations, the evidence of any probabilistic program, and therefore\nof any graphical model, can be optimized with respect to an arbitrary subset of\nits sampled variables. To carry out this optimization, we develop the first\nBayesian optimization package to directly exploit the source code of its\ntarget, leading to innovations in problem-independent hyperpriors, unbounded\noptimization, and implicit constraint satisfaction; delivering significant\nperformance improvements over prominent existing packages. We present\napplications of our method to a number of tasks including engineering design\nand parameter optimization.","url_abs":"http://arxiv.org/abs/1707.04314v1","url_pdf":"http://arxiv.org/pdf/1707.04314v1.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":"bayesian-optimization-for-probabilistic","repo_url":"https://github.com/probprog/bopp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"bayesian-optimization-for-probabilistic","repo_url":"https://github.com/probprog/deodorant","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"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}