Partial Markov Categories
arXiv:2502.03477
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.
Extended version of "Evidential Decision Theory via Partial Markov Categories", arXiv:2301.12989. Improved presentation of exact observations