RegrKm SE prediction with nugget currently predicts the epistemic uncertainty (uncertainty of the mean prediction) + aleatoric uncertainty (nugget SE, uncertainty that the model sees as random error). E.g. sampling lots of points with SE 0.1 noise and fitting a GP through them gives us
Where the GP goes through the points, the epistemic uncertainty is relatively low (it is the mean of a large sample) but the aleatoric uncertainty has SE 0.1 (nugget estimate).
What instead would be interesting would be the epistemic uncertainty alone
mllg
commented
3 years ago
RegrKm SE prediction with nugget currently predicts the epistemic uncertainty (uncertainty of the mean prediction) + aleatoric uncertainty (nugget SE, uncertainty that the model sees as random error). E.g. sampling lots of points with SE 0.1 noise and fitting a GP through them gives us
Where the GP goes through the points, the epistemic uncertainty is relatively low (it is the mean of a large sample) but the aleatoric uncertainty has SE 0.1 (nugget estimate).
What instead would be interesting would be the epistemic uncertainty alone
This is just
I suggest we introduce a hyperparameter that gives the option to predict this, could be interesting for MBO.