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.

翻译：我们引入部分马尔可夫范畴作为合成概率推断的合成框架，将Cho与Jacobs、Fritz以及Golubtsov在马尔可夫范畴方面的工作，与Cockett和Lack在笛卡尔限制范畴方面的工作相结合。我们以纯粹的范畴论术语描述了观测、贝叶斯定理、归一化，以及Pearl更新和Jeffrey更新。