Advancing Bayesian Chronological Modelling for Multi-period Sites in the West African Forest Zone

The construction of reliable chronological frameworks for multi-period archaeological sites is a fundamental prerequisite for testing hypotheses about cultural change, migration, and human-environment interaction, yet the radiocarbon dating programmes applied to West African forest zone sites rarely employ the full statistical power available through Bayesian modelling approaches that integrate stratigraphic information with radiometric data. This dissertation advances Bayesian chronological modelling methodology for application to the specific challenges posed by multi-period forest zone sites, including the old wood problem, the plateau in the Southern Hemisphere calibration curve affecting dates in the range 400-200 BCE, and the complex stratigraphic conditions generated by bioturbation, intensive human redeposition, and root penetration in tropical contexts. The methodological programme is developed and tested using existing and new radiocarbon datasets from four multi-period sites in the Nigerian forest zone, including Iwo Eleru in Ondo State, Afikpo in Ebonyi State, and two newly dated sites in Delta State. For each site, optimal sample selection criteria, outlier modelling approaches, and stratigraphically constrained prior construction are developed and evaluated through systematic sensitivity analysis. Application of the refined modelling framework produces chronological estimates significantly more precise than those yielded by conventional unconstrained calibration, reducing ninety-five percent credible intervals by an average of thirty-eight percent. The dissertation presents a practical modelling guide and software workflow documentation optimized for Nigerian archaeological contexts, making advanced Bayesian chronology accessible to practitioners without specialist statistical training. Keywords: Bayesian chronological modelling, radiocarbon dating, West African forest zone, multi-period sites, chronological methodology.