{"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/parametric-gaussian-process-regression-for","title":"Parametric Gaussian Process Regression for Big Data","arxiv_id":"1704.03144","date":"2017-04-11","proceeding":null,"authors":["Maziar Raissi"],"abstract":"This work introduces the concept of parametric Gaussian processes (PGPs),\nwhich is built upon the seemingly self-contradictory idea of making Gaussian\nprocesses parametric. Parametric Gaussian processes, by construction, are\ndesigned to operate in \"big data\" regimes where one is interested in\nquantifying the uncertainty associated with noisy data. The proposed\nmethodology circumvents the well-established need for stochastic variational\ninference, a scalable algorithm for approximating posterior distributions. The\neffectiveness of the proposed approach is demonstrated using an illustrative\nexample with simulated data and a benchmark dataset in the airline industry\nwith approximately 6 million records.","url_abs":"http://arxiv.org/abs/1704.03144v2","url_pdf":"http://arxiv.org/pdf/1704.03144v2.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":"parametric-gaussian-process-regression-for","repo_url":"https://github.com/maziarraissi/ParametricGP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"variational-inference","task_name":"Variational Inference"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}