Partial Markov Categories

Published 24 Jan 2025 in math.CT and cs.LO | (2502.03477v1)

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.

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