A Bayesian alternative for aoristic analyses in archaeology.
Aoristic analysis is often used to handle chronological uncertainties of datasets where scientific dates (e.g., 14C and OSL) are unavailable, and observations are described by association to archaeological periods or phases. Although several advances have been made over the last 2 decades, the basic...
| Publicado en: | Archaeometry Vol. 67; pp. 7 - 31 |
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| Autor principal: | |
| Formato: | Literature Review |
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Wiley-Blackwell
Jun2025 Supplement 1
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| 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=185619665&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 185619665 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 0003813X D7X jtl: Archaeometry issn: 0003813X maglogo: Y pubinfo: dt: Jun2025 Supplement 1 vid: 67 pid: 480 pub: Wiley-Blackwell artinfo: ui: 185619665 10.1111/arcm.12984 ppf: 7 ppct: 24 formats: tig: atl: A Bayesian alternative for aoristic analyses in archaeology. aug: au: Crema, Enrico R. affil: McDonald Institute for Archaeological Research, University of Cambridge, Cambridge, UK Department of Archaeology, University of Cambridge, Cambridge, UK su: Sampling errors Bayesian field theory Archaeology Probability theory sug: subj: Sampling errors Bayesian field theory Archaeology Probability theory keyword: aoristic analyses archaeological periodisation Bayesian inference chronological uncertainty ab: Aoristic analysis is often used to handle chronological uncertainties of datasets where scientific dates (e.g., 14C and OSL) are unavailable, and observations are described by association to archaeological periods or phases. Although several advances have been made over the last 2 decades, the basic principle of this approach remains fundamentally the same. Temporal windows of analyses are first divided into regularly sized time blocks, and probability weight is assigned to each of these for every observation. Weights are then aggregated by time block, and the resulting vector of summed probabilities is interpreted as a curve representing changes in the intensity over time of a particular phenomenon. This paper reviews the basic principles and assumptions of aoristic analyses in archaeology, highlighting several issues with its application and interpretation, advocating for a Bayesian alternative implemented via baorista, a new package written in R statistical computing language. The robustness of the proposed solution is evaluated through a series of experiments based on simulated datasets, which showcase key advantages over aoristic analysis. Two specific solutions are considered: a parametric approach where data are fitted to specific growth models and a nonparametric approach that allows for the visualisation of the changing frequencies of events, accounting for sampling error and the peculiarities of archaeological periodisation. pubtype: Academic Journal doctype: Literature Review src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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