Radiocarbon dating machine calibration
It offers basic calibration functions as well as a suite of statistical tests for examining aggregated calibrated dates, generally referred to as summed probability distributions of radiocarbon dates (SPDs, or sometimes SPDRDs).function, which uses the probability density approach (Stuiver and Reimar 1993, Van Der Plicht 1993, Bronk Ramsey 2008) implemented in most calibration software (e.g.The scripts below examines the transition when the declining growth rate exhibits a short reversion (i.e. The plot function requires the definition of an argument, which indicates what needs to be plotted (either the results of the statistical tests or the local estimates of geometric growth rates).The scripts below examines the transition when the declining growth rate exhibits a short reversion (i.e.6500-6000 to 6000-5500 cal BP).The resulting set of radiocarbon dates can then be calibrated and aggregated in order to generate an expected SPD of the fitted model that takes into account idiosyncrasies of the calibration process.This process can be repeated times to generate a distribution of SPDs (which takes into account the effect of sampling error) that can be compared to the observed data.From a practical point a “bin” represents an “phase” or episode of occupation, but clearly this is problematic definition in case of a continuous occupation.
Shennan et al 2013 (Timpson et al 2014 for more detail and methodological refinement) introduced a Monte-Carlo simulation approach consisting of a three stage process: 1) fit a growth model to the observed SPD, for example via regression; 2) generate random samples from the fitted model; and 3) uncalibrate the samples.
Using normalised or non-normalised calibrations does not have an impact on the shape of individual calibrated probability distribution, but does influence the shape of SPDs, so we suggest that at minimum a case study should explore whether its results differ much when normalised versus unnormalised dates are used.
SPDs can be potentially biased if there is strong inter-site variability in sample size, for example where one well-resourced research project has sampled one particular site for an unusual number of dates.
A seminal paper by John Rick some 30 years ago (1987) first introduced the idea of using the frequency of archaeological radiocarbon dates through time as a proxy for highs and lows in human population dynamics.
The increased availability of large collection of archaeological (especially anthropogenic) radiocarbon dates have dramatically pushed this research agenda forward in recent years.