Pymc3 for graphical models (PGM)

Questions
#1

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 several nodes connected with edges based on my prior knowledge, but without exact CPDs known. And I want to use a dataset to derive CPDs from it. Is this possible at all?
I read about Bayesian network + Machine Learning binding, but couldn’t find any actual examples.
I will be grateful for any advises or references.

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#2

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).

Note that the diagram that the pymc3 won’t necessarily look quite as you’d draw the Bayes net - there’s a bit of discussion at the end of that question around that.

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#3

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.

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#4

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?

#5

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

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#6

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

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