A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
Description of changes:
This PR augments the functionality of the ProductBrownian kernel for quadrature by adding an offset parameter.
Before, the kernel was defined as min(x, x') for x, x'>=0, now, it is defined as min(x-c, x'-c) for any c in Re and x, x'>=c. This is useful for domains that require negative input values. The default c=0 restores the original functionality
Tests are adjusted accordingly.
By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
Issue #, if available:
Description of changes: This PR augments the functionality of the
ProductBrowniankernel forquadratureby adding anoffsetparameter.Before, the kernel was defined as min(x, x') for x, x'>=0, now, it is defined as min(x-c, x'-c) for any c in Re and x, x'>=c. This is useful for domains that require negative input values. The default
c=0restores the original functionalityTests are adjusted accordingly.
By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.