To create a reliable radiocarbon calibration curve, one needs not only high-quality data but also a robust statistical methodology. The unique aspects of much of the calibration data provide considerable modeling challenges and require a made-to-measure approach to curve construction that accurately represents and adapts to these individualities, bringing the data together into a single curve. For IntCal20, the statistical methodology has undergone a complete redesign, from the random walk used in IntCal04, IntCal09 and IntCal13, to an approach based upon Bayesian splines with errors-in-variables. The new spline approach is still fitted using Markov Chain Monte Carlo (MCMC) but offers considerable advantages over the previous random walk, including faster and more reliable curve construction together with greatly increased flexibility and detail in modeling choices. This paper describes the new methodology together with the tailored modifications required to integrate the various datasets. For an end-user, the key changes include the recognition and estimation of potential over-dispersion in14C determinations, and its consequences on calibration which we address through the provision of predictive intervals on the curve; improvements to the modeling of rapid14C excursions and reservoir ages/dead carbon fractions; and modifications made to, hopefully, ensure better mixing of the MCMC which consequently increase confidence in the estimated curve.

中文翻译：

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放射性碳校准曲线构建的 IntCal20 方法：使用贝叶斯样条和变量误差的新方法

要创建可靠的放射性碳校准曲线，不仅需要高质量的数据，还需要稳健的统计方法。许多校准数据的独特方面提供了相当大的建模挑战，并且需要一种定制的曲线构造方法，该方法可以准确地表示和适应这些个性，将数据汇集到一条曲线中。对于 IntCal20，统计方法经过了彻底的重新设计，从 IntCal04、IntCal09 和 IntCal13 中使用的随机游走，到基于贝叶斯样条和变量误差的方法。新的样条方法仍然使用马尔可夫链蒙特卡罗（MCMC）进行拟合，但与之前的随机游走相比具有相当大的优势，包括更快、更可靠的曲线构造以及在建模选择中大大增加的灵活性和细节。本文描述了新方法以及集成各种数据集所需的定制修改。对于最终用户，关键变化包括识别和估计潜在的过度分散14C 测定及其对校准的影响，我们通过在曲线上提供预测区间来解决；快速建模的改进14C偏移和储层年龄/死碳分数；并进行了修改，希望能确保更好地混合 MCMC，从而增加对估计曲线的信心。