Abstract
<abstract> <p>Radiocarbon ages must be calibrated due to the remarkable fluctuations of the atmospheric radiocarbon level. The traditional method (e.g., Calib) does not make use of any constraint such as the temporal/stratigraphical ordering of the ages, thereby resulting in one or several large age ranges. Bayesian age modeling is advantageous over the traditional method in several aspects. First, it can provide precise age estimates by applying some constraints known <italic>a priori</italic>. Second, it may provide a timing of an archaeological feature or a geological event that is unable to be dated directly. Although several Bayesian age modeling frameworks have been developed, inexperienced users may need not only a more user-friendly environment for data entry and definition of their project-specific problem, but also a powerful post-processing tool for analyzing and visualizing the results. Here a hierarchical Bayesian model with a minimum level of structural complexity is presented. It provides users with a flexible and powerful framework to incorporate radiocarbon ages into a sequence along a one-dimensional continuum so that it best reveals their temporal order, thereby yielding a more precise timing. The accompanying Matlab software package not only complements the existing MatCal package designed to calibrate radiocarbon ages individually, but also serves as an alternative to the online tools of Bayesian radiocarbon age modeling such as OxCal and BCal.</p> </abstract>
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