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The explanation of what happens is simple: the weights are proportional to the inverse of the variances, so that 0-errors would result in NaN. I'm not sure what's the best solution, apart from special-casing 0, but also how? Do you have any concrete suggestion of how to address this problem?
weightedmean([1 ± 0, 1 ± 0])
returnsNaN ± 0
, but1 ± 0
is expected. Could you give me a better solution? Thank you very much.The text was updated successfully, but these errors were encountered: