@fehiepsi Thank you for the response
I looked up the GPR code, it is very helpful. (and I am finally able to appreciate of usefulness of Pyro.
For class that subclass of Reparameterize, we only need to create a guide function that
1. call pyro.sample with delta function,
2. return all the parameters, is that correct?
If that's the case, there is only question remaining is that I need to do MAP inference for both my kernel and the preference vector. However, I am not sure how to incorporate such abstract data, since we never have any single 'observation', apart from slider manipulation (which contains 3 observations and is only inferred with a BTL model.