{"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-data-analysis-with","title":"Probabilistic Data Analysis with Probabilistic Programming","arxiv_id":"1608.05347","date":"2016-08-18","proceeding":null,"authors":["Feras Saad","Vikash Mansinghka"],"abstract":"Probabilistic techniques are central to data analysis, but different\napproaches can be difficult to apply, combine, and compare. This paper\nintroduces composable generative population models (CGPMs), a computational\nabstraction that extends directed graphical models and can be used to describe\nand compose a broad class of probabilistic data analysis techniques. Examples\ninclude hierarchical Bayesian models, multivariate kernel methods,\ndiscriminative machine learning, clustering algorithms, dimensionality\nreduction, and arbitrary probabilistic programs. We also demonstrate the\nintegration of CGPMs into BayesDB, a probabilistic programming platform that\ncan express data analysis tasks using a modeling language and a structured\nquery language. The practical value is illustrated in two ways. First, CGPMs\nare used in an analysis that identifies satellite data records which probably\nviolate Kepler's Third Law, by composing causal probabilistic programs with\nnon-parametric Bayes in under 50 lines of probabilistic code. Second, for\nseveral representative data analysis tasks, we report on lines of code and\naccuracy measurements of various CGPMs, plus comparisons with standard baseline\nsolutions from Python and MATLAB libraries.","url_abs":"http://arxiv.org/abs/1608.05347v1","url_pdf":"http://arxiv.org/pdf/1608.05347v1.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-data-analysis-with","repo_url":"https://github.com/probcomp/cgpm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"probabilistic-programming","task_name":"Probabilistic Programming"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}