Bayesian inference for a wavefront model of the Neolithisation of Europe

TL;DR

This paper develops a Bayesian wavefront model for the spread of Neolithic culture in Europe, incorporating geographic effects and waterways, and employs Gaussian process emulators to efficiently estimate model parameters from radiocarbon data.

Contribution

It introduces a novel numerical scheme and Gaussian process emulators to enable practical Bayesian inference for a complex wavefront model of Neolithisation.

Findings

Estimated parameters with uncertainty quantification.

Identified radiocarbon sites inconsistent with the model.

Enhanced model efficiency with Gaussian process emulators.

Abstract

We consider a wavefront model for the spread of Neolithic culture across Europe, and use Bayesian inference techniques to provide estimates for the parameters within this model, as constrained by radiocarbon data from Southern and Western Europe. Our wavefront model allows for both an isotropic background spread (incorporating the effects of local geography), and a localized anisotropic spread associated with major waterways. We introduce an innovative numerical scheme to track the wavefront, and use Gaussian process emulators to further increase the efficiency of our model, thereby making Markov chain Monte Carlo methods practical. We allow for uncertainty in the fit of our model, and discuss the inferred distribution of the parameter specifying this uncertainty, along with the distributions of the parameters of our wavefront model. We subsequently use predictive distributions, taking account of parameter uncertainty, to identify radiocarbon sites which do not agree well with our model. These sites may warrant further archaeological study, or motivate refinements to the model.