Are there any examples for intercausal reasoning using pymc3?
For example, given this graph (or any for that matter, this is just first one coming into mind).
We can model it, have some priors, observe for example WetGrass, then we can calculate (sample) posteriors.(lets assume this is learning, model training)
But, how would you compute P(Cloudy=True | Sprinkler=True)=? and enable some interface to user.
Any continues example, not only discrete or even better combined would be great.