Does GPy and GPflow share a common mathematical background? I'm asking this because I'm using GPy but I cannot see the references. However, GPflow provides references in its examples.
Is it Ok using keep using GPy or would you suggest the use GPflow inmediately for gaussian processes purposes?
GPy and GPflow definitely share a common mathematical background: Gaussian processes Rasmussen and Williams, and many of the concepts are very similar in both frameworks: kernels, likelihoods, mean-functions, inducing points, etc. For me, the biggest difference between GPy and GPflow is the computational backend: AFAIK GPy uses plain Python and numpy to perform all its computations, whereas GPflow relies on TensorFlow. This gives GPflow multiple nice features for free: GPU acceleration, automatic gradients, compatibility with TF eco-system, etc. Depending on your use-case, these features can be crucial or simply nice-to-have.
Here is more information on the technical details between the two frameworks: https://gpflow.readthedocs.io/en/master/intro.html#what-s-the-difference-between-gpy-and-gpflow