METHODS FOR SUMMARIZING RADIOCARBON DATASETS
METHODS FOR SUMMARIZING RADIOCARBON DATASETS
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DOI：
10.1017/rdc.2017.108
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发表时间：
2017-12-01
期刊：
影响因子：
8.3
通讯作者：
Ramsey, Christopher Bronk
中科院分区：
文献类型：
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作者：
Ramsey, Christopher Bronk
贝叶斯模型在分析来自特定地点的放射性碳（C-14）测量的大型数据集以及区域文化或政治模型方面非常强大。这些模型需要被描述的基础过程的先验，包括基础事件的分布。时序信息也被纳入DNA研究中使用的贝叶斯模型，使用天际线图来显示人口趋势。尽管取得了这些进展，但在评估数据是否符合假设的基础模型以及处理Sum图中看到的伪影类型方面仍然存在困难。此外，现有方法不适用于无法量化底层过程的情况，或者样本选择被认为以掩盖原始事件分布的方式过滤了数据的情况。本文比较了三种不同的方法：“总和”分布，假设未注明日期的事件，核密度的方法。他们在OxCal程序的实施进行了描述和可视化的结果，从时间和地理分析考虑的情况下，没有有用的先验信息的适用性。结论是核密度分析是一种功能强大的方法，可以更广泛地应用于广泛的测年应用。
Bayesian models have proved very powerful in analyzing large datasets of radiocarbon (C-14) 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.