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Kernel vs GP composition #182
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Hi! Glad to hear you're trying the two approaches out. There's no issue per-se in taking either approach. Certainly, you can recover the process that you get by writing For example, writing something like f = @gppp begin
f1 = GP(SEKernel())
f2 = GP(Matern12Kernel())
f3 = f1 + f2
end lets you write things like f_post = posterior(f(GPPPInput(:f3, x), y)
f_post(GPPPInput(:f2, x)) to get the posterior over Does this help? |
Thanks a lot, that's very helpful! In any case being able to add GPs is great functionality. How about |
It's a similar story with f = @gppp begin
f1 = GP(SEKernel())
f2 = stretch(f1, 0.5)
end
GP(SEKernel() ∘ ScaleTransform(0.5)) (I might be off by a constant factor) It's probably easier to get at |
That makes sense. I too am less convinced by this example, but thanks very much for the clarification! |
Haha glad you agree. I'm keen to get differentiation / integration working in addition to addition etc, but haven't had the time. These are the kinds of things that I think would also be very convincing, and potentialy have quite a lot of utility. |
These would definitely be great ideas and would look great in any demo. |
Hi there, I am trying to get into Stheno a little but I'm having a little trouble figuring out whether there are any notable differences between some transformations applied to GPs vs. kernels and mean functions. Examples are
stretch
vs.KernelFunctions.ScaleTransform
, orGP + GP
vs.Kernel + Kernel
andMean + Mean
(I assume this would be useful when combined different likelihood functions). Would there be any loss eg. in definingstretch
to work via input transforms on the kernel and mean functions?The text was updated successfully, but these errors were encountered: