Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Model calibration #222

Open
ghost opened this issue Jun 12, 2019 · 6 comments
Open

Model calibration #222

ghost opened this issue Jun 12, 2019 · 6 comments

Comments

@ghost
Copy link

ghost commented Jun 12, 2019

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?

@javiergonzalezh
Copy link
Contributor

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,

@javiergonzalezh
Copy link
Contributor

Note that for the GPflow model wrapper you don't need to start with all the interfaces. IModel and IDifferentiable shpuld give you good coverage already.

@ghost
Copy link
Author

ghost commented Jun 13, 2019

Hello, thanks for replying.
What I would like to do is retrieve posterior distributions of uncertain model parameters based on experimental observations. The statistical model:

y = eta(x,theta) + delta(x) + epsilon,

where
y - output (measurable by experiment)
eta - original model output
x - settings (measurable by experiment)
theta - unknown model parameters
delta - systematic model bias
epsilon - noise.

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.

@apaleyes
Copy link
Collaborator

apaleyes commented Sep 5, 2019

Hey @szboksteen , did you end up using emukit? If so, we would love to know your use case!

@ghost
Copy link
Author

ghost commented Sep 5, 2019 via email

@apaleyes
Copy link
Collaborator

apaleyes commented Sep 5, 2019

@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

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants