当前位置: X-MOL 学术Journal of Archaeological Science › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
End-to-end Bayesian analysis for summarizing sets of radiocarbon dates
Journal of Archaeological Science ( IF 2.8 ) Pub Date : 2021-09-15 , DOI: 10.1016/j.jas.2021.105473
Michael Holton Price 1 , José M. Capriles 2 , Julie A. Hoggarth 3 , R. Kyle Bocinsky 4, 5 , Claire E. Ebert 6 , James Holland Jones 7
Affiliation  

Archaeologists and demographers increasingly employ aggregations of published radiocarbon (14C) dates as demographic proxies summarizing changes in human activity in past societies. Presently, summed probability densities (SPDs) of calibrated radiocarbon dates are the dominant method of using 14C dates to reconstruct demographic trends. Unfortunately, SPDs are incapable of converging on the distribution that generated a set of radiocarbon measurements, even when the number of observations is large. To overcome this problem, we propose a more principled alternative that combines finite mixture models and end-to-end Bayesian inference. Numerical simulations and an assessment of the statistical identifiability of our method demonstrate that it correctly converges on the generating distribution for two important models, exponentials and finite Gaussian mixtures, at least if the same statistical model is used to fit the data as was used to generate the data. To further validate this approach, we apply it to a set of radiocarbon dates from the Maya city of Tikal. We show that an end-to-end approach reconstructs with high accuracy expert demographic reconstructions based on settlement patterns and ceramics, but with more precise time-resolution and characterization of uncertainty than has heretofore been possible. Future work should consider alternatives to finite Gaussian mixtures for fitting the generating distribution.



中文翻译:

用于总结放射性碳日期集的端到端贝叶斯分析

考古学家和人口学家越来越多地使用已发表的放射性碳 ( 14 C) 日期的聚合作为总结过去社会人类活动变化的人口代理。目前,校准放射性碳日期的总概率密度 (SPD) 是使用14C 日期以重建人口趋势。不幸的是,即使观测数量很大,SPD 也无法收敛于产生一组放射性碳测量值的分布。为了克服这个问题,我们提出了一个更有原则的替代方案,它结合了有限混合模型和端到端贝叶斯推理。数值模拟和我们方法的统计可识别性评估表明,它正确地收敛于两个重要模型(指数模型和有限高斯混合模型)的生成分布,至少如果使用与生成数据相同的统计模型来拟合数据数据。为了进一步验证这种方法,我们将其应用于一组来自玛雅蒂卡尔市的放射性碳日期。我们展示了一种端到端的方法可以基于聚落模式和陶瓷进行高精度的专家人口重建,但具有比以往更精确的时间分辨率和不确定性表征。未来的工作应该考虑替代有限高斯混合来拟合生成分布。

更新日期:2021-09-15
down
wechat
bug