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Summary:
This update adds a surrogate model version that can handle numerical errors observed as
nan
orinf
values in the discrepancies between observed and simulated data. This is implemented as a new model class RobustGPyRegression. The two main differences between this and standard GPyRegression are that this version (1) does not use evidence points with non-finite output value to update the regression model and (2) can train a GPy classifier to predict whether given input parameters result in a finite output value or not. These capabilities are demonstrated here.The new model can be combined with the unreliable simulations wrapper #484 to handle failed simulations in BOLFI as discussed in #433.
Please make sure
If your contribution adds, removes or somehow changes the functional behavior of the package, please check that
make lint
,make docs
andmake test
and the proposed changes pass all unit tests (check step 6 of CONTRIBUTING.rst for details)
Copyright and Licensing
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