I have the following bayesian hierarchical graphical (DAG) structure where all the edges are directed downward, i.e. A --> C,D; D–> H,I etc.
(A) (B) / \ / \ / \ / \ (C ) ( D ) ( E ) ( F ) / \ / \ / \ / \ / \ / \ / \ / \ ( G ) ( H ) ( I ) ( J ) ( K ) ( L )
I have their co-occurence count as follows:-
I want to train a bayesian probabilistic inference model as per the defined graph structure so that it can learn from the above co-occurence matrix and I can get insights such as:-
- P(D,F| G=1,J=1)
Can I do that using edward? If yes then can you suggest a piece of code to do that.
As of now I tried pgmpy,libpgm,pomegranate,pymc3. But they are hardly scaling for big data.
Thanks in advance.