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New Methods and Critical Aspects in Bayesian Mathematics for 14C Calibration

Published online by Cambridge University Press:  18 July 2016

Peter Steier,
Werner Rom and
Stephan Puchegger
Peter Steier
Affiliation:
Vienna Environmental Research Accelerator, Institut für Isotopenforschung und Kernphysik, Universität Wien, Währinger Straße 17, A-1090 Wien, Austria. Email: peter.steier@univie.ac.at.
Werner Rom
Affiliation:
AMS 14C Dating Laboratory, Institut for Fysik og Astronomi Aarhus Universitet, DK-8000 Ärhus C, Denmark
Stephan Puchegger
Affiliation:
Vienna Environmental Research Accelerator, Institut für Isotopenforschung und Kernphysik, Universität Wien, Währinger Straße 17, A-1090 Wien, Austria. Email: peter.steier@univie.ac.at.
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Abstract

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The probabilistic radiocarbon calibration approach, which largely has replaced the intercept method in 14C dating, is based on the so-called Bayes' theorem (Bayes 1763). Besides single-sample calibration, Bayesian mathematics also supplies tools for combining 14C results of many samples with independent archaeological information such as typology or stratigraphy (Buck et al. 1996). However, specific assumptions in the “prior probabilities”, used to transform the archaeological information into mathematical probability distributions, may bias the results (Steier and Rom 2000). A general technique for guarding against such a bias is “sensitivity analysis”, in which a range of possible prior probabilities is tested. Only results that prove robust in this analysis should be used. We demonstrate the impact of this method for an assumed, yet realistic case of stratigraphically ordered samples from the Hallstatt period, i.e. the Early Iron Age in Central Europe.

Information

Type
II. Getting More from the Data
Information
Radiocarbon , Volume 43 , Issue 2A: Proceedings of the 17th International Radiocarbon Conference (Part 1 of 3) , 2001, pp. 373 - 380
Copyright
Copyright © The Arizona Board of Regents on behalf of the University of Arizona 

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