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This is meant to be a starting point for a discussion on adding tutorials on how to use calibrate emulate sample with simple examples to build intuition.
In particular PR adds a simple example showing how to use EKI to minimize two loss functions
L(x,y) = (x-1)^2 + (y+1)^2
L(x,y) = (x^2-1)^2 + (y+1)^2
This is accomplished by taking the forward map to just be the loss function. The former has a minimum at (x,y) = (1,-1) and the latter has two minima at (x,y) = (+1,-1) and (x,y) = (-1,-1). By switching the random seed on the latter, one can converge to either minimum. Eventually, such simple examples could be included in the docs
Thanks for starting this! I'd be keen to chat more about some tutorial-type examples. At present we only have the EnsembleKalmanProcesses/runtests (EKP/runtests) for the ensemble methods (in isolation) - with similar loss functions. I have been wondering about where the divide sits between runtests and examples - and i think it would be beneficial to have these as simple tutorials - so that we can relegate the runtests to more unit-style function testing.
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This is meant to be a starting point for a discussion on adding tutorials on how to use calibrate emulate sample with simple examples to build intuition.
In particular PR adds a simple example showing how to use EKI to minimize two loss functions
This is accomplished by taking the forward map to just be the loss function. The former has a minimum at (x,y) = (1,-1) and the latter has two minima at (x,y) = (+1,-1) and (x,y) = (-1,-1). By switching the random seed on the latter, one can converge to either minimum. Eventually, such simple examples could be included in the docs