Methods for Summarizing Radiocarbon Datasets

Abstract Bayesian models have proved very powerful in analyzing large datasets of radiocarbon ( 14 C) measurements from specific sites and in regional cultural or political models. These models require the prior for the underlying processes that are being described to be defined, including the distribution of underlying events. Chronological information is also incorporated into Bayesian models used in DNA research, with the use of Skyline plots to show demographic trends. Despite these advances, there remain difficulties in assessing whether data conform to the assumed underlying models, and in dealing with the type of artifacts seen in Sum plots. In addition, existing methods are not applicable for…

• SR
  Sight Research UK

  Award: NRCF010002
• NE
  Natural Environment Research Council

  Award: NRCF010002