# The Past as a Stochastic Process

arXiv: https://arxiv.org/abs/2112.05876 · HTML: https://mlsanity.com/paper/2112.05876/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 111 references that are in the map.
- First posted on arXiv 11 Dec 2021.

## Builds on (most cited first, 3 of 3)

- [Deeptime: a Python library for machine learning dynamical models from time series data](https://mlsanity.com/paper/2110.15013/) (2021)
- [Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching](https://mlsanity.com/paper/1807.05748/) (2018)
- [Individuals, Institutions, and Innovation in the Debates of the French Revolution](https://mlsanity.com/paper/1710.06867/) (2017)

## Abstract (arXiv)

Historical processes manifest remarkable diversity. Nevertheless, scholars have long attempted to identify patterns and categorize historical actors and influences with some success. A stochastic process framework provides a structured approach for the analysis of large historical datasets that allows for detection of sometimes surprising patterns, identification of relevant causal actors both endogenous and exogenous to the process, and comparison between different historical cases. The combination of data, analytical tools and the organizing theoretical framework of stochastic processes complements traditional narrative approaches in history and archaeology.