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Bayesian Inference of Calibration Curves: Application to Archaeomagnetism

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Tools for Constructing Chronologies

Part of the book series: Lecture Notes in Statistics ((LNS,volume 177))

Summary

This chapter focuses on recently developed models for the analysis and interpretation of archaeomagnetic dating evidence. Archaeomagnetic data from archaeological structures such as hearths, kilns or sets of bricks and tiles, exhibit considerable experimental errors and are typically also associated with date estimates from other sources such as stratigraphic sequences, historical records or chronometric methods. This chapter summarizes the technical aspects of recent Bayesian statistical modelling work, describing a hierarchical model for the archaeomagnetic data and its uncertainties and combining this with models of the other dating evidence, based on those described by Buck (Chapter 1), to create a calibration curve for future archaeomagnetic dating work in a locality. The proposed model and inference methods are illustrated by the const ruction of a calibration curve using recently published archaeomagnetic data from Lübeck, Germany. With this new posterior estimate of the curve available, it is then possible to use the Bayesian statistical framework to estimate the calendar dates of undated archaeological features.

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Authors
  1. Philippe Lanos
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Editors and Affiliations

  1. Department of Probability and Statistics, University of Sheffield, Hicks Building Hounsfield Road, S3 7RH, Sheffield, UK

    Caitlin E. Buck

  2. Department of Archaeology, University of Durham, South Road, DH1 3LE, Durham, UK

    Andrew R. Millard

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© 2004 Springer-Verlag London

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Lanos, P. (2004). Bayesian Inference of Calibration Curves: Application to Archaeomagnetism. In: Buck, C.E., Millard, A.R. (eds) Tools for Constructing Chronologies. Lecture Notes in Statistics, vol 177. Springer, London. https://doi.org/10.1007/978-1-4471-0231-1_3

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  • DOI: https://doi.org/10.1007/978-1-4471-0231-1_3

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-763-6

  • Online ISBN: 978-1-4471-0231-1

  • eBook Packages: Springer Book Archive

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