Bradshaw and Bayes

Towards a Timetable for the Neolithic

Bibliographic Data

ID6044166
AuthorsAlex Bayli (0000-0003-2782-1979, English Heritage, corresponding author), Christopher B Ramsey (0000-0002-8641-9309, University of Oxford), Johannes Van Der Plicht (0000-0003-4298-7037, University of Groningen), A Whittle (0000-0001-6811-8724, Cardiff University)
Year2007
Volume17
IssueS1
Pages1-28
Publication date2007-02-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueCambridge Archaeological Journal (JOURNAL)
Journal identifiersISSN: 0959-7743 • E-ISSN: 1474-0540
PublisherCambridge University Press (CUP) (PUBLISHER)
DOI10.1017/s0959774307000145
OpenAlexW2061144112
LanguageEN
Citations received225

The importance of chronology is reasserted as a means to achieving history and a sense of temporality. A range of current methods for estimating the dates and durations of archaeological processes and events are considered, including visual inspection of graphs and tables of calibrated dates and the summing of the probability distributions of calibrated dates. These approaches are found wanting. The Bayesian statistical framework is introduced, and a worked example presents simulated radiocarbon dates as a demonstration of the explicit, quantified, probabilistic estimates now possible on a routine basis. Using this example, the reliability of the chronologies presented for the five long barrows considered in this series of papers is explored. It is essential that the ‘informative’ prior beliefs in a chronological model are correct. If they are not, the dating suggested by the model will be incorrect. In contrast, the ‘uninformative’ prior beliefs have to be grossly incorrect before the outputs of the model are importantly wrong. It is also vital that the radiocarbon ages included in a model are accurate, and that their errors are correctly estimated. If they are not, the dating suggested by a model may also be importantly wrong. Strenuous effort and rigorous attention to archaeological and scientific detail are inescapable if reliable chronologies are to be built. The dates presented in the following papers are based on models. ‘All models are wrong, some models are useful’ (Box 1979, 202). We hope readers will find them useful, and will employ ‘worry selectivity’ to determine whether and how each model may be importantly wrong. The questions demand the timetable, and our prehistories deserve both

Archaeology · Bayes' theorem · Bayesian inference · Bayesian probability · Chronology · Prior probability · Probabilistic logic · Radiocarbon dating · Statistical model · Archaeology and ancient environmental studies · Computer Science · Engineering · History · Image Processing and 3D Reconstruction · Artificial Intelligence

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