Estimating the number of casualties in the American Indian war: a Bayesian analysis using the power law distribution

TL;DR

This paper employs Bayesian methods to estimate total casualties of the American Indian war by modeling observed data with a power law distribution, accounting for errors and omissions in historical records.

Contribution

It introduces a Bayesian framework that incorporates power law distribution modeling and error sources to infer total casualties in historical conflicts.

Findings

Casualty counts follow a power law distribution.

Bayesian analysis accounts for data errors and omissions.

Estimated total casualties provide new historical insights.

Abstract

The American Indian war lasted over one hundred years, and is a major event in the history of North America. As expected, since the war commenced in late eighteenth century, casualty records surrounding this conflict contain numerous sources of error, such as rounding and counting. Additionally, while major battles such as the Battle of the Little Bighorn were recorded, many smaller skirmishes were completely omitted from the records. Over the last few decades, it has been observed that the number of casualties in major conflicts follows a power law distribution. This paper places this observation within the Bayesian paradigm, enabling modelling of different error sources, allowing inferences to be made about the overall casualty numbers in the American Indian war.