Open basaks opened 6 years ago
If I had a dataset where the measurement uncertainty varies across various measurement points, how can I take that into account?
I am not sure, and haven't looked at the underlying math for some time. Hoping @bsmurphy would know the answer.
I imagine, supporting user provided weights (sample dependent) could be a way to specify sample uncertainty but again I'm not sure what's the convention for this in other krigging software..
I've been thinking about this, and to my knowledge taking into account heterogeneous data errors (or even a specific uniform error) isn't clearly defined in the usual math behind kriging. I don't think putting the error values on the diagonal of the kriging matrix is the right way to handle this (although I could be wrong). Also not sure how to implement data weighting, although I need to think about that more. Seems like some kind of Monte Carlo thing might in fact be the best way go -- run a bunch of kriging realizations with data randomly perturbed within the error bars and then look at the spread of the resulting kriged values.
I need to think about this more tho, and do some research/reading. One way or another, handling uncertainty in measurements (as in #30, at least by implementing continuous part kriging to smooth out the nugget effect) would be great for v2.
If I had a dataset where the measurement uncertainty varies across various measurement points, how can I take that into account?
From the
execute
doctring in OrdinaryKrigingQuestions:
nugget
parameter of the variogram estimated?