Bayesian perspectives on the discovery of the Higgs particle.
It is argued that the high degree of trust in the Higgs particle before its discovery raises the question of a Bayesian perspective on data analysis in high energy physics in an interesting way that differs from other suggestions regarding the deployment of Bayesian strategies in the field.
| Publicado en: | Synthese Vol. 194; no. 2; pp. 377 - 395 |
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| Formato: | Artículo |
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Springer Nature
Feb2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=121163194&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 121163194 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Feb2017 vid: 194 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 121163194 10.1007/s11229-015-0943-6 ppf: 377 ppct: 18 formats: fmt: @attributes: type: P size: 433KB tig: atl: Bayesian perspectives on the discovery of the Higgs particle. aug: au: Dawid, Richard affil: Center for Mathematical Philosophy , Ludwig Maximilian University Munich , Geschwister Scholl Platz 1 Munich Germany su: Higgs bosons Bayesian analysis Particles (Nuclear physics) Data analysis Probability theory sug: subj: Higgs bosons Bayesian analysis Particles (Nuclear physics) Data analysis Probability theory keyword: Bayesianism Confirmation Discovery Frequentism Higgs particle High energy physics Non-empirical evidence ab: It is argued that the high degree of trust in the Higgs particle before its discovery raises the question of a Bayesian perspective on data analysis in high energy physics in an interesting way that differs from other suggestions regarding the deployment of Bayesian strategies in the field. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2017. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2017 holdings: @attributes: islocal: N |
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