# Bayesian inference with Monte Carlo approximation: Measuring regional differentiation in ceramic and glass vessel assemblages in Republican Italy, ca. 200 BCE - 20 CE

## TL;DR

This paper introduces a Bayesian Monte Carlo method using Hellinger distance to compare archaeological assemblages as probability distributions, applied to study regional food habits in Republican Italy around 200 BCE to 20 CE.

## Contribution

It presents a novel approach combining Bayesian inference, Monte Carlo approximation, and Hellinger distance for analyzing archaeological assemblages.

## Key findings

- Quantifies regional differentiation in ceramic and glass vessel assemblages.
- Provides insights into social and cultural changes in Republican Italy.
- Demonstrates the method's applicability to archaeological data analysis.

## Abstract

Methods of measuring differentiation in archaeological assemblages have long been based on attribute-level analyses of assemblages. This paper considers a method of comparing assemblages as probability distributions via the Hellinger distance, as calculated through a Dirichlet-categorical model of inference using Monte Carlo methods of approximation. This method has application within practice-theory traditions of archaeology, an approach which seeks to measure and associate different factors that comprise the habitus of society. It is implemented here focusing on the question of regional food consumption habits in Republican Italy in the last two centuries BCE, toward informing a perspective on mass social change.