For many years now, archaeologists have postulated that the presence or absence of various artefact types within excavated features should give insight as to their relative dates of deposition even when stratigraphic information is not present. A typical data set used in such studies can be reported as a cross-classification table (often called an abundance matrix or, equivalently, a contingency table) of excavated features against artefact types. Each entry of the table represents the number of a particular artefact type found in a particular archaeological feature. Methodologies for attempting to identify temporal sequence on the basis of such data are commonly referred to as seriation techniques.
Several different procedures for seriation including both parametric and non-parametric statistics have been used in an attempt to reconstruct relative chronological orders on the basis of such contingency tables. In this paper we develop a number of possible model-based approaches that might be used to aid in relative, archaeological chronology building. We use the recently developed Markov chain Monte Carlo method based on Langevin diffusions to fit some of the proposed models. Predictive Bayesian model choice techniques are then employed to ascertain which of the models we develop are most plausible. We illustrate our methodology with two examples taken from the literature on archaeological seriation.
This is due to appear in Applied Statistics . Please email me for further information.