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Model calibration #222
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Hi @szboksteen, Thanks for your interest in Emukit. Can you provide some details about what you are trying to do? Having an specific example will help us to identify better what is missing. Also, have you try to start with a wrapper for GPflow like the one we have for GPy? This will give you direct access to all the stuff already implemented. Definitely something nice to have, |
Note that for the GPflow model wrapper you don't need to start with all the interfaces. |
Hello, thanks for replying. y = eta(x,theta) + delta(x) + epsilon, where The result of calibration is P(theta | x_observed,y_observed) = posterior. This can then be used as en emulator to make predictions P(y_new | x_new, theta_posterior). Thanks for the advise about creating a wrapper. |
Hey @szboksteen , did you end up using emukit? If so, we would love to know your use case! |
Hello Andrei,
I have used emukit only for DOE at the moment. I have directly used GPFlow
for building the emulators. If I find a way to do Bayesian Calibration
(Kennedy and O'Hagan type) with emukit, then I will definitely use it more.
Any progress on this?
Regards
Sowande
…On Thu, Sep 5, 2019 at 10:19 AM Andrei Paleyes ***@***.***> wrote:
Hey @szboksteen <https://github.com/szboksteen> , did you end up using
emukit? If so, we would love to know your use case!
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@szboksteen no, not really. we are currently focusing on other applications of emukit, i guess mainly because we haven't encountered use case for calibration ourselves |
Dear Emukit developper(s),
I recently found the Emukit library and I really like it. There is one feature that would make it 100% complete for what I need: Bayesian calibration (as in https://www.asc.ohio-state.edu/statistics/comp_exp/jour.club/KennedyOHagan_2002.pdf)
Do you know of any example/application of this method in Emukit or any other package based on GPFlow?
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