A Bayesian central equivalent dose model for optically stimulated luminescence dating

In this study, we propose and implement a Bayesian model to estimate a central equivalent dose from a set of luminescence measurements. This model is based on assumptions similar to the ones used in the standard statistical pipeline (typically implemented in the Analyst software followed by a subsequent central equivalent dose analysis) but tackles some of its main limitations. More specifically, it consists of a three-stage hierarchical model that has two main advantages over the standard approach: first, it avoids the introduction of auxiliary variables (typically mean and variance), at each step of the inference process, which are likely to fail to characterise the distributions of interest; second, it ensures a homogeneous and consistent inference with respect to the overall model and data. As a Bayesian model, our model requires the specification of prior distributions; we discuss such informative and non-informative distributions and check the relevance of our choices on synthetic data. Then, we use data derived from Single Aliquot and Regenerative (SAR) dose measurements performed on single grains from laboratory-bleached and dosed samples. The results show that our Bayesian approach offers a promising alternative to the standard one. Finally, we conclude by stressing that, relying on a Bayesian hierarchical model, our approach could be modified to incorporate additional information (e.g. stratigraphic constraints) that is difficult to formalise properly with the existing approaches.