I try to use gaussian processes for modeling (mostly smoothing and interpolation) data from measurements, which can have very different uncertainties. These uncertainties are known, but can vary a lot: e.g. some points have error +-1, some others +-20. In cases with all measurements having about the same error gaussian processes perform well, but when not - they either give too high noise estimates or give too much weight to bad points.What are the options to take these errors into account? I read something about modeling errors as a nested gaussian process, but this doesn't fit my case: errors for all (including nearby) points are unrelated.
Gaussian processes with varying uncertainties
regression;gaussian;interpolation