Archaeological data provide a potential to investigate the diffusion of technological and cultural traits. However, much of this research agenda currently needs more formal quantitative methods to address small sample sizes and chronological uncertainty. This paper introduces a novel Bayesian framework for inferring the shape of diffusion curves using radiocarbon data associated with the presence/absence of a particular innovation. We developed two distinct approaches: 1) a hierarchical model that enables the fitting of an s-shaped diffusion curve whilst accounting for inter-site variations in the probability of sampling the innovation itself, and 2) a non-parametric model that can estimate the changing proportion of the innovation across user-defined time-blocks. The robustness of the two approaches was first tested against simulated datasets and then applied to investigate three case studies, the first pair on the diffusion of farming in prehistoric Japan and Britain and the third on cycles of changes in the burial practices of later prehistoric Britain.

中文翻译：

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使用放射性碳数据模拟创新曲线的扩散

考古数据提供了研究技术和文化特征传播的潜力。然而，目前这项研究议程的大部分内容需要更正式的定量方法来解决小样本量和时间顺序的不确定性。本文介绍了一种新颖的贝叶斯框架，用于使用与特定创新的存在/不存在相关的放射性碳数据来推断扩散曲线的形状。我们开发了两种不同的方法：1）分层模型，可以拟合 s 形扩散曲线，同时考虑创新本身抽样概率的站点间差异；2）非参数模型，可以估计改变用户定义的时间段内的创新比例。这两种方法的稳健性首先针对模拟数据集进行了测试，然后应用于调查三个案例研究，第一对是关于史前日本和英国农业传播的案例，第三对是关于史前英国后期埋葬习俗变化周期的案例研究。