What hyperparameter optimisation method is used by GPy

[kentcov]

I couldn't find anything about the technique used by GPy when optimising the length-scales in an RBF kernel with ARD=True.

I'm aware of Log-likelihood optimisation (methods such as conjugate gradient)
or Integration via Hybrid Monte Carlo methods

(Both detailed in Williams & Rasmussen - Gaussian Processes for Regression 1996)

is one of these used, or is there another approach being used here?

kind regards,

[xorb0ss]

I believe it's Limited-memory BFGS (L-BFGS) or a variation.

[lawrennd]

By default it's maximum likelihood by L-BFGS, but you can change the optimizer and there are details on HMC in this notebook: http://nbviewer.jupyter.org/github/SheffieldML/notebook/blob/master/GPy/sampling_hmc.ipynb

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On Tue, Sep 25, 2018 at 7:34 PM xorb0ss ***@***.***> wrote: I believe it's Limited-memory BFGS (L-BFGS) or a variation. — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#680 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AAIKWg-OXs9izok24r7Q5f1IjtZv9s1Xks5uendOgaJpZM4W06FM> .