The core idea of Bayesian approaches consists in updating our degree of belief in the truth of a hypothesis in light of new pieces of evidence pertaining to it. It is a form of incremental induction wherein the belief at the end of an investigative step is injected as a prior belief for the next step. This new belief will in turn be modified by conditionalizing upon new evidence. In order to reach credibility interval for drawing conclusions about the temporal setting of the RE, we were compelled to apply several informative priors on our final BEAST analysis. In the first step it is beneficial to place a weakly informative prior on the root [56] . This prior obtained from the literature had the effect of concentrating the probability density around its mean so it could be captured by a narrow shaped gamma distribution. In the following steps, formal probability distributions were retrieved from posteriors in the previous step and used as prior assumptions about rates and node antiquity. Although, the overlap of datasets between iterations was kept minimal, our strategy imposes to maintain some sequences across datasets in order to identify the nodes to which the derived prior should be applied. The reduction of the credibility intervals for the date parameters indicates that our approach succeeded to improve the accuracy of the time estimates by combining different lines of information coming from informative data and from the literature.