Buck, C.E. orcid.org/0000-0002-0872-9504 and Juarez, M.A. (2024) Bayesian radiocarbon modelling for beginners. Archaeometry. ISSN 0003-813X
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Author(s). Archaeometry published by John Wiley & Sons Ltd on behalf of University of Oxford. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Jun 2024 14:59 |
Last Modified: | 20 Jun 2024 11:57 |
Status: | Published online |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1111/arcm.12998 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:212885 |
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