Bayesian models for relative archaeological chronology building

For many years, archaeologists have postulated that the numbers of various artefact types found within excavated features should give insight about th eir relative dates of deposition even when stratigraphic information is not present. A typical data set used in such studies can be reported as a cros s-classification table (often called an abundance matrix or, equivalently, a contingency table) of excavated features against artefact types. Each ent ry of the table represents the number of a particular artefact type found i n a particular archaeological feature. Methodologies for attempting to iden tify temporal sequences on the basis of such data are commonly referred to as seriation techniques. Several different procedures for seriation includi ng both parametric and non-parametric statistics have been used in an attem pt to reconstruct relative chronological orders on the basis of such contin gency tables. We develop some possible model-based approaches that might be used to aid in relative, archaeological chronology building. We use the re cently developed Markov chain Monte Carlo method based on Langevin diffusio ns to fit some of the models proposed. Predictive Bayesian model choice tec hniques are then employed to ascertain which of the models that we develop are most plausible. We analyse two data sets taken from the literature on a rchaeological seriation.