Methods for Summarizing Radiocarbon Datasets

2017/11/20 by Christopher Bronk Ramsey · 1,170 citations
Biochemistry, Genetics and Molecular Biology · Earth and Planetary Sciences · Mathematics · Social Sciences · #Archaeology and ancient environmental studies #Artificial intelligence #Bayesian probability #Computer science #Data mining #Event (particle physics) #Forensic and Genetic Research #Geology #Kernel (algebra) #Kernel density estimation #Mathematics #Pacific and Southeast Asian Studies #Paleontology #Radiocarbon dating #Range (aeronautics) #Statistics

paper · pdf · doi:10.1017/rdc.2017.108

published in Radiocarbon 59(6), 1809-1833 (Cambridge University Press)

openalex publication_date 2017/11/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

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 situations where it is not possible to quantify the underlying process, or where sample selection is thought to have filtered the data in a way that masks the original event distribution. In this paper three different approaches are compared: “Sum” distributions, postulated undated events, and kernel density approaches. Their implementation in the OxCal program is described and their suitability for visualizing the results from chronological and geographic analyses considered for cases with and without useful prior information. The conclusion is that kernel density analysis is a powerful method that could be much more widely applied in a wide range of dating applications.

Citations

• Remarks on Some Nonparametric Estimates of a Density Function
• On Estimation of a Probability Density Function and Mode
• Recent and Planned Developments of the Program OxCal
• IntCal13 and Marine13 Radiocarbon Age Calibration Curves 0–50,000 Years cal BP
• Bayesian Analysis of Radiocarbon Dates

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