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Robust Bayesian Analysis, an Attempt to Improve Bayesian Sequencing

Published online by Cambridge University Press:  18 July 2016

Franz Weninger
Affiliation:
Vienna Environmental Research Accelerator (VERA), Faculty of Physics, Isotope Research, University of Vienna, Währinger Straße 17, A-1090 Vienna, Austria
Peter Steier
Affiliation:
Vienna Environmental Research Accelerator (VERA), Faculty of Physics, Isotope Research, University of Vienna, Währinger Straße 17, A-1090 Vienna, Austria
Walter Kutschera
Affiliation:
Vienna Environmental Research Accelerator (VERA), Faculty of Physics, Isotope Research, University of Vienna, Währinger Straße 17, A-1090 Vienna, Austria
Eva Maria Wild
Affiliation:
Vienna Environmental Research Accelerator (VERA), Faculty of Physics, Isotope Research, University of Vienna, Währinger Straße 17, A-1090 Vienna, Austria
Corresponding
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Abstract

Bayesian sequencing of radiocarbon dates deals with the problem that in most cases there does not exist an unambiguous way to define the so-called prior function, which represents information in addition to the result of the 14C measurements alone. However, a random choice of a particular prior function can lead to biased results. In this paper, “robust Bayesian analysis,” which uses a whole set of prior functions, is introduced as a more reliable method. The most important aspects of the mathematical foundation and of the practical realization of the method are described. As a general result, robust Bayesian analysis leads to higher accuracy, but paid for with reduced precision. Our investigations indicate that it seems possible to establish robust analysis for practical applications.

Type
Calibration, Data Analysis, and Statistical Methods
Copyright
Copyright © 2010 by the Arizona Board of Regents on behalf of the University of Arizona 

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