GPy and GPflow mathematical background - references

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?

Solution 1:

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

Solution 2:

That would depend on what you are actually doing. The very basic GPs should be similar, just that GPflow relies on tensorflow for the gradients (if used) and probably some technical implementation differences.

For the other more advanced models, both libraries provide references to the respective papers in the docs. In my opinion, GPflow's design is mainly centered around the SVGP framework from [1] and [2] (and many other extensions.. I can really recommend [2] if you are interested in the theory). But they still do provide some other implementations.

I use GPflow since it works on the GPU and offers a lot of state-of-the-art implementations. However, the disadvantage would be that it is under a lot of change.

If you want to use classic GPs and are not too concerned with performance or very up-to-date methods I'd say GPy should be sufficient and the more stable variant.

[1] Hensman, James, Alexander Matthews, and Zoubin Ghahramani. "Scalable variational Gaussian process classification." (2015).

[2] Matthews, Alexander Graeme de Garis. Scalable Gaussian process inference using variational methods. Diss. University of Cambridge, 2017.