Our data-driven model for stars, The Cannon, is a regression. That is, it figures out how the labels generate the spectral pixels with a model for possible functional forms for that generation. I spent part of today building a Jupyter notebook to demonstrate that—when the assumptions underlying the regression are correct—the results of the regression are accurate (and precise). That is, the maximum-likelihood regression estimator is a good one. That isn't surprising; there are very general proofs; but it answers some questions (that my collaborators have) about cases where the labels (the regressors) are correlated in the training set.
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