Is pymc3 suitable for implementation of PGMs? Everywhere in tutorials they show toy examples of a water-sprinkler or student network with only categorical values and known conditional probabilities (CPD), so, basically, the PGM network is established by prior knowledge. What if I “draw” a PGM with…

I’ve used it for a simple Bayes net, and am planning on doing some more complicated ones - there’s an example here . There’s a couple more links to other examples in my original question, but I it sounds like my example is the closest to what you’re after (I had a lot of trouble finding examples too)…

Yep - currently we have not implemented any specific algorithm for inferencing PGM (like loopy belief propagation https://en.m.wikipedia.org/wiki/Belief_propagation ), so you might need to rely on random forward sample for these use case.

Thanks for replying. I am not very familiar with this domain, just thought there should already be some API for this use-case. May be there are other toolboxes which have this functionality?

Thanks, there were some interesting posts. I also found that Netica software from Norsys can learn relations from data directly: https://www.norsys.com/WebHelp/NETICA/X_Quick_Tour_Learn_Data.htm

Pymc3 for graphical models (PGM)

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AlexAndorra August 17, 2020, 9:06am 5

Hi Andrey,
I think you’ll find interesting resources about this in this thread.
Hope this helps

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Pymc3 for graphical models (PGM)

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