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Bayesian models for relative archaeological chronology building

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  • Caitlin E. Buck
  • Sujit K. Sahu

Abstract

For many years, archaeologists have postulated that the numbers of various artefact types found within excavated features should give insight about 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 sequences 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. We develop some 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 models proposed. Predictive Bayesian model choice techniques 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 archaeological seriation.

Suggested Citation

  • Caitlin E. Buck & Sujit K. Sahu, 2000. "Bayesian models for relative archaeological chronology building," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(4), pages 423-440.
  • Handle: RePEc:bla:jorssc:v:49:y:2000:i:4:p:423-440
    DOI: 10.1111/1467-9876.00203
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    Cited by:

    1. Congdon, P., 2005. "Bayesian predictive model comparison via parallel sampling," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 735-753, April.
    2. Halekoh, U. & Vach, W., 2004. "A Bayesian approach to seriation problems in archaeology," Computational Statistics & Data Analysis, Elsevier, vol. 45(3), pages 651-673, April.
    3. Gabriela Czibula & Iuliana M Bocicor & Istvan-Gergely Czibula, 2013. "Temporal Ordering of Cancer Microarray Data through a Reinforcement Learning Based Approach," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-12, April.
    4. van de Velden, Michel & Groenen, Patrick J.F. & Poblome, Jeroen, 2009. "Seriation by constrained correspondence analysis: A simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3129-3138, June.

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