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GP hyper-parameter optimization for BMC #540

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mmahsereci opened this issue Sep 27, 2021 · 0 comments
Open

GP hyper-parameter optimization for BMC #540

mmahsereci opened this issue Sep 27, 2021 · 0 comments
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feature request Requests for features to be implemented quad Issues related to quadrature

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@mmahsereci
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Is your feature request related to a problem? Please describe.
The current Bayesian Monte Carlo (BMC) implementation does not support hyper-parameter optimization. Thus, it is of little practical value right now.

Describe the solution you'd like.
It would be great if the GP-model could fit it's kernel hyper-parameters e.g. by ML type II.

Additional context
I suppose it' not clear yet how gradients are computed in ProbNum which are needed to optimize the marginal likelihood to obtain the optimal hyper-parameters. I suppose that is a blocker for this Issue. So mainly opening this Issue for visibility in case someone uses BMC already.
@mmahsereci mmahsereci added feature request Requests for features to be implemented quad Issues related to quadrature labels Sep 27, 2021
@mmahsereci mmahsereci mentioned this issue Sep 28, 2021
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