Chronological uncertainty complicates attempts to use radiocarbon dates as proxies for processes such as human population growth/decline, forest fires and marine ingression. Established approaches involve turning databases of radiocarbon‐date densities into single summary proxies that cannot fully account for chronological uncertainty. Here, I use simulated data to explore an alternative Bayesian approach that instead models the data as what they are, namely radiocarbon‐dated event counts. The approach involves assessing possible event‐count sequences by sampling radiocarbon date densities and then applying a Markov Chain Monte Carlo method to estimate the parameters of an appropriate count‐based regression model. The regressions based on individual sampled sequences were placed in a multilevel framework, which allowed for the estimation of hyperparameters that account for chronological uncertainty in individual event times. Two processes were used to produce simulated data. One represented a simple monotonic change in event‐counts and the other was based on a real palaeoclimate proxy record. In both cases, the method produced estimates that had the correct sign and were consistently biased towards zero. These results indicate that the approach is widely applicable and could form the basis of a new class of quantitative models for use in exploring long‐term human and environmental processes.

中文翻译：

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评估贝叶斯放射性碳事件计数（REC）模型以研究长期的人类和环境过程

时间不确定性使使用放射性碳数据作为人口增长/下降，森林火灾和海洋入侵等过程的代理人的尝试变得更加复杂。既定的方法涉及将放射性碳日期密度数据库转换为不能完全说明时间顺序不确定性的单一摘要代理。在这里，我使用模拟数据来探索另一种贝叶斯方法，该方法将数据按其本身进行建模，即放射性碳日期事件计数。该方法涉及通过采样放射性碳日期密度来评估可能的事件计数序列，然后应用马尔可夫链蒙特卡罗方法来估计适当的基于计数的回归模型的参数。基于各个采样序列的回归被放置在一个多层次的框架中，它允许估计超参数，这些超参数考虑了单个事件时间中的时间不确定性。使用两个过程来生成模拟数据。一个代表事件计数的简单单调变化，另一个代表真实的古气候代理记录。在这两种情况下，该方法产生的估计值均具有正确的符号，并且始终偏向零。这些结果表明，该方法是广泛适用的，并且可以构成一类用于探索长期人类和环境过程的新型定量模型的基础。在这两种情况下，该方法产生的估计值均具有正确的符号，并且始终偏向零。这些结果表明，该方法是广泛适用的，并且可以构成一类用于探索长期人类和环境过程的新型定量模型的基础。在这两种情况下，该方法产生的估计值均具有正确的符号，并且始终偏向零。这些结果表明，该方法是广泛适用的，并且可以构成一类用于探索长期人类和环境过程的新型定量模型的基础。