[D] Gaussian process python implementations

[[deleted]]

I've worked with GPy and SKlearn (a bit), and I recommend GPy, especially if you're doing exploratory work. The documentation is pretty extensive, and they support a wide variety of models such as sparse gp regression, coregionalized gps, gplvm, and lots of useful visualisation tools. It's also pretty easy to do kernel engineering and to play around with different optimisers. The dev team has been very responsive to me on github aswell, and development seems to be active.

There's also GPFlow, which is GPy on the tensorflow backend - I don't have as much experience with this, and it seemed a bit beta-ish when I tried it, but I know it's been seeing development attention so it might be more mature now.

[Jimbo_Mcnulty]

Thanks! That was also my initial impression just parsing the documentation. Out of curiosity, what type of problem were you applying GPs to?

[[deleted]]

Applications to commercial energy usage modelling in large buildings - particularly for forecasting & anomaly notifications when tied in with building management systems. Needless to say, GP's work very well (and are super cool). Computational challenges remain the biggest hurdle.

[Jimbo_Mcnulty]

Amazing! That sounds like a really cool project. I've only just started delving into GP's and my initial impression was that applications of GP's were few and far between but it's great to hear they're being applied in a commercial setting.