Thanks a lot @twiecki!
Indeed, some of the models I am using are discrete but I also have a group of continuous models in hand and am definitely interested in a Bayesian inference approach.
Let me rephrase my question a bit since I might have given a wrong impression of what I am attempting. I am actually interested in getting a full posterior over the model parameters (and then using the most probable ones).
My main concern at the moment is that I do not know if there is a way to either use my existing models in conjunction with PyMC3 (i.e. can a simple python function be introduced as the model structure) or, if not, to re-write those models in “PyMC3 notation” to get the parameters posterior.
I might be making matters worse with my “explanation” so please feel free to ask for any clarifications or further details.
thanks.