{"title":"Bayesian radiocarbon modelling for beginners","authors":"Caitlin E. Buck, Miguel A. Juárez","doi":"10.1111/arcm.12998","DOIUrl":null,"url":null,"abstract":"Due to freely available, tailored software, Bayesian statistics is now the dominant paradigm for archaeological chronology construction in the UK and much of Europe and is increasing in popularity in the Americas. Such software provides users with powerful tools for Bayesian inference for chronological models with little need to undertake formal study of statistical modelling or computer programming. This runs the risk that it is reduced to the status of a black box, which is not sensible given the power and complexity of the modelling tools it implements. In this paper we seek to offer intuitive insight to ensure that readers from the archaeological research community who use Bayesian chronological modelling software will be better able to make well educated choices about the tools and techniques they adopt. Our hope is that they will then be both better informed about their own research designs and better prepared to offer constructively critical assessments of the modelling undertaken by others.","PeriodicalId":8254,"journal":{"name":"Archaeometry","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archaeometry","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1111/arcm.12998","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"ARCHAEOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Due to freely available, tailored software, Bayesian statistics is now the dominant paradigm for archaeological chronology construction in the UK and much of Europe and is increasing in popularity in the Americas. Such software provides users with powerful tools for Bayesian inference for chronological models with little need to undertake formal study of statistical modelling or computer programming. This runs the risk that it is reduced to the status of a black box, which is not sensible given the power and complexity of the modelling tools it implements. In this paper we seek to offer intuitive insight to ensure that readers from the archaeological research community who use Bayesian chronological modelling software will be better able to make well educated choices about the tools and techniques they adopt. Our hope is that they will then be both better informed about their own research designs and better prepared to offer constructively critical assessments of the modelling undertaken by others.
期刊介绍：
Archaeometry is an international research journal covering the application of the physical and biological sciences to archaeology, anthropology and art history. Topics covered include dating methods, artifact studies, mathematical methods, remote sensing techniques, conservation science, environmental reconstruction, biological anthropology and archaeological theory. Papers are expected to have a clear archaeological, anthropological or art historical context, be of the highest scientific standards, and to present data of international relevance.
The journal is published on behalf of the Research Laboratory for Archaeology and the History of Art, Oxford University, in association with Gesellschaft für Naturwissenschaftliche Archäologie, ARCHAEOMETRIE, the Society for Archaeological Sciences (SAS), and Associazione Italian di Archeometria.
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