Thanks, Jon. I will work on this. One of my first tasks in my causal reasoning job was to write Java classes and methods and and applet that used them to allow a user to interactively enter a DAG and then enter pairs of nodes and return the d-separation facts for each pair. Chris Meek who is mentioned by Baez defined the task for me.
--- Frank C. Wimberly 140 Calle Ojo Feliz, Santa Fe, NM 87505 505 670-9918 Santa Fe, NM On Thu, Feb 18, 2021, 9:20 AM jon zingale <[email protected]> wrote: > 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/ >
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