{"title":"使用贝叶斯参数估计从无黑盒数据中学习更多知识","authors":"Rachel C. Kurchin","doi":"10.1038/s42254-024-00698-0","DOIUrl":null,"url":null,"abstract":"In an age of expensive experiments and hype around new data-driven methods, researchers understandably want to ensure they are gleaning as much insight from their data as possible. Rachel C. Kurchin argues that there is still plenty to be learned from older approaches without turning to black boxes.","PeriodicalId":19024,"journal":{"name":"Nature Reviews Physics","volume":"6 3","pages":"152-154"},"PeriodicalIF":44.8000,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using Bayesian parameter estimation to learn more from data without black boxes\",\"authors\":\"Rachel C. Kurchin\",\"doi\":\"10.1038/s42254-024-00698-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In an age of expensive experiments and hype around new data-driven methods, researchers understandably want to ensure they are gleaning as much insight from their data as possible. Rachel C. Kurchin argues that there is still plenty to be learned from older approaches without turning to black boxes.\",\"PeriodicalId\":19024,\"journal\":{\"name\":\"Nature Reviews Physics\",\"volume\":\"6 3\",\"pages\":\"152-154\"},\"PeriodicalIF\":44.8000,\"publicationDate\":\"2024-02-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Reviews Physics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.nature.com/articles/s42254-024-00698-0\",\"RegionNum\":1,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PHYSICS, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Reviews Physics","FirstCategoryId":"101","ListUrlMain":"https://www.nature.com/articles/s42254-024-00698-0","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHYSICS, APPLIED","Score":null,"Total":0}
引用次数: 0
摘要
在这个实验昂贵、新数据驱动方法大行其道的时代，研究人员希望从数据中获得尽可能多的洞察力，这是可以理解的。雷切尔-库尔钦（Rachel C. Kurchin）认为，在不使用黑盒的情况下，仍然可以从旧方法中学到很多东西。
Using Bayesian parameter estimation to learn more from data without black boxes
In an age of expensive experiments and hype around new data-driven methods, researchers understandably want to ensure they are gleaning as much insight from their data as possible. Rachel C. Kurchin argues that there is still plenty to be learned from older approaches without turning to black boxes.
期刊介绍：
Nature Reviews Physics is an online-only reviews journal, part of the Nature Reviews portfolio of journals. It publishes high-quality technical reference, review, and commentary articles in all areas of fundamental and applied physics. The journal offers a range of content types, including Reviews, Perspectives, Roadmaps, Technical Reviews, Expert Recommendations, Comments, Editorials, Research Highlights, Features, and News & Views, which cover significant advances in the field and topical issues. Nature Reviews Physics is published monthly from January 2019 and does not have external, academic editors. Instead, all editorial decisions are made by a dedicated team of full-time professional editors.
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