{"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/nested-kriging-predictions-for-datasets-with","title":"Nested Kriging predictions for datasets with large number of observations","arxiv_id":"1607.05432","date":"2016-07-19","proceeding":null,"authors":["Didier Rullière","Nicolas Durrande","François Bachoc","Clément Chevalier"],"abstract":"This work falls within the context of predicting the value of a real function\nat some input locations given a limited number of observations of this\nfunction. The Kriging interpolation technique (or Gaussian process regression)\nis often considered to tackle such a problem but the method suffers from its\ncomputational burden when the number of observation points is large. We\nintroduce in this article nested Kriging predictors which are constructed by\naggregating sub-models based on subsets of observation points. This approach is\nproven to have better theoretical properties than other aggregation methods\nthat can be found in the literature. Contrarily to some other methods it can be\nshown that the proposed aggregation method is consistent. Finally, the\npractical interest of the proposed method is illustrated on simulated datasets\nand on an industrial test case with $10^4$ observations in a 6-dimensional\nspace.","url_abs":"http://arxiv.org/abs/1607.05432v3","url_pdf":"http://arxiv.org/pdf/1607.05432v3.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":"nested-kriging-predictions-for-datasets-with","repo_url":"https://github.com/drulliere/nestedKriging","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"LGPL-3.0"}}],"tasks":[],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1607.05432","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}