{"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/a-critical-review-of-statistical","title":"A critical review of statistical calibration/prediction models handling data inconsistency and model inadequacy","arxiv_id":"1611.04376","date":"2016-11-14","proceeding":null,"authors":["Pascal Pernot","Fabien Cailliez"],"abstract":"Inference of physical parameters from reference data is a well studied problem with many intricacies (inconsistent sets of data due to experimental systematic errors, approximate physical models...). The complexity is further increased when the inferred parameters are used to make predictions (virtual measurements) because parameters uncertainty has to be estimated in addition to parameters best value. The literature is rich in statistical models for the calibration/prediction problem, each having benefits and limitations. We review and evaluate standard and state-of-the-art statistical models in a common bayesian framework, and test them on synthetic and real datasets of temperature-dependent viscosity for the calibration of Lennard-Jones parameters of a Chapman-Enskog model.","url_abs":"http://arxiv.org/abs/1611.04376v2","url_pdf":"http://arxiv.org/pdf/1611.04376v2.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"a-critical-review-of-statistical","repo_url":"https://github.com/ppernot/CalPred","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1611.04376","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}