In an attempt to ground my above thoughts, and respond to your apprehension, I found these two John Baez articles offering some explanation for why one might want to use category theory to guide Bayesian network calculations. Additionally, I was pleased to see that he makes explicit the connection to monoidal categories[2].
[1] https://golem.ph.utexas.edu/category/2018/07/bayesian_networks.html#:~:text=in%20causal%20theory.-,Introduction,nodes%2C%20satisfying%20the%20Markov%20condition. [2] https://golem.ph.utexas.edu/category/2018/01/a_categorical_semantics_for_ca.html#more For my own part, the intuition to approach the problem of separating actual *causes* from collections of *evidence* via presheaves mostly follows from choosing to study the epimorphisms associated with such networks. The sections are going to need to compose, forming *narratives* (not sure what else to call these) that give _a_ lineage of causes. It strikes me (a tourist) that such a framework could be useful when reasoning about evidence. -- Sent from: http://friam.471366.n2.nabble.com/ - .... . -..-. . -. -.. -..-. .. ... -..-. .... . .-. . FRIAM Applied Complexity Group listserv Zoom Fridays 9:30a-12p Mtn GMT-6 bit.ly/virtualfriam un/subscribe http://redfish.com/mailman/listinfo/friam_redfish.com FRIAM-COMIC http://friam-comic.blogspot.com/ archives: http://friam.471366.n2.nabble.com/
