ahbarnett
commented
1 year ago Hello, We haven't got to parameter optimization yet, partly because EFGP doesn't provide a fast algorithm for likelihood evaluation. However, as we mention in the preprint, cross-validation on held-out data is still possible. Since there are only 2-4 parameters (\sigma, \ell, maybe \nu, maybe k(0)), simple finite-differencing could be used to estimate parameter-derivatives of the held-out RMSE. Another idea is use GPyTorch to estimate parameters on a subset of data, then use EFGP for the large-scale problem. That's all I recommend for now. Maybe you can invent a faster way to optimize parameters? Best, Alex
On Fri, Jul 7, 2023 at 8:17 AM sleepingPhD @.***> wrote:
Is it possible to optimize the hyperparameter in Matlab? Or hou can I just calculate them before regression?
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Is it possible to optimize the hyperparameter in Matlab? Or hou can I just calculate them before regression?