{"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/being-bayesian-in-the-2020s-opportunities-and","title":"Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics","arxiv_id":"2211.10029","date":"2022-11-18","proceeding":null,"authors":["Joshua J. Bon","Adam Bretherton","Katie Buchhorn","Susanna Cramb","Christopher Drovandi","Conor Hassan","Adrianne L. Jenner","Helen J. Mayfield","James M. McGree","Kerrie Mengersen","Aiden Price","Robert Salomone","Edgar Santos-Fernandez","Julie Vercelloni","Xiaoyu Wang"],"abstract":"Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most statisticians and data scientists. Whether they are dedicated Bayesians or opportunistic users, applied professionals can now reap many of the benefits afforded by the Bayesian paradigm. In this paper, we touch on six modern opportunities and challenges in applied Bayesian statistics: intelligent data collection, new data sources, federated analysis, inference for implicit models, model transfer and purposeful software products.","url_abs":"https://arxiv.org/abs/2211.10029v2","url_pdf":"https://arxiv.org/pdf/2211.10029v2.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":"being-bayesian-in-the-2020s-opportunities-and","repo_url":"https://github.com/john-wang1015/abcandsnl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}