Inference for population dynamics in the Neolithic period

TL;DR

This paper develops a statistical framework to estimate the spread of Neolithic farming in Europe by modeling radiocarbon data with a wavefront approach, accounting for uncertainties and computational challenges.

Contribution

It introduces Gaussian process emulators to efficiently infer parameters of a wavefront model for Neolithic spread, addressing computational costs and data uncertainties.

Findings

Successful validation with predictive simulations

Effective modeling of spatial deviations using Gaussian processes

Robust parameter estimation despite data uncertainties

Abstract

We consider parameter estimation for the spread of the Neolithic incipient farming across Europe using radiocarbon dates. We model the arrival time of farming at radiocarbon-dated, early Neolithic sites by a numerical solution to an advancing wavefront. We allow for (technical) uncertainty in the radiocarbon data, lack-of-fit of the deterministic model and use a Gaussian process to smooth spatial deviations from the model. Inference for the parameters in the wavefront model is complicated by the computational cost required to produce a single numerical solution. We therefore employ Gaussian process emulators for the arrival time of the advancing wavefront at each radiocarbon-dated site. We validate our model using predictive simulations.