On being a good Bayesian.
Bayesianism is fast becoming the dominant paradigm in archaeological chronology construction. This paradigm shift has been brought about in large part by widespread access to tailored computer software which provides users with powerful tools for complex statistical inference with little need to lea...
| Publicado en: | World Archaeology Vol. 47; no. 4; pp. 567 - 585 |
|---|---|
| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
Oct2015
|
| Materias: | |
| 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=109226874&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 109226874 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00438243 WAR jtl: World Archaeology issn: 00438243 maglogo: Y pubinfo: dt: Oct2015 vid: 47 iid: 4 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 109226874 10.1080/00438243.2015.1053977 ppf: 567 ppct: 18 formats: tig: atl: On being a good Bayesian. aug: au: Buck, Caitlin E. Meson, Bo affil: School of Mathematics and Statistics, University of Sheffield Independent Scholar su: Historical chronology Bayesian analysis Radiocarbon dating Philosophy of science Computer programming sug: subj: Historical chronology Bayesian analysis Radiocarbon dating Philosophy of science Computer programming keyword: archaeological theory Bayesian inference ethics radiocarbon dating Scientific method ab: Bayesianism is fast becoming the dominant paradigm in archaeological chronology construction. This paradigm shift has been brought about in large part by widespread access to tailored computer software which provides users with powerful tools for complex statistical inference with little need to learn about statistical modelling or computer programming. As a result, we run the risk that such software will be reduced to the status of black boxes. This would be a dangerous position for our community since good, principled use of Bayesian methods requires mindfulness when selecting the initial model, defining prior information, checking the reliability and sensitivity of the software runs and interpreting the results obtained. In this article, we provide users with a brief review of the nature of the care required and offer some comments and suggestions to help ensure that our community continues to be respected for its philosophically rigorous scientific approach. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2015 holdings: @attributes: islocal: N |
|---|