Partial Markov Categories
Category Theory
2026-02-23 v4 Logic in Computer Science
Abstract
We introduce partial Markov categories as a synthetic framework for synthetic probabilistic inference, blending the work of Cho and Jacobs, Fritz, and Golubtsov on Markov categories with the work of Cockett and Lack on cartesian restriction categories. We describe observations, Bayes' theorem, normalisation, and both Pearl's and Jeffrey's updates in purely categorical terms.
Cite
@article{arxiv.2502.03477,
title = {Partial Markov Categories},
author = {Elena Di Lavore and Mario Román and Paweł Sobociński},
journal= {arXiv preprint arXiv:2502.03477},
year = {2026}
}
Comments
Extended version of "Evidential Decision Theory via Partial Markov Categories", arXiv:2301.12989. Improved presentation of exact observations