The Paired Swap Permutation Test is an exact non-parametric procedure that compares explanatory power of two dependent predictors via symmetric within-subject swapping for categorical data and ECDF mapping for continuous data.
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A deep learning method amortizes probabilistic XCO2 retrieval from OCO-2 spectra via Laplace approximations and normalizing flows, trained on simulations with model errors to achieve faster inference and better-calibrated uncertainties than operational solvers.
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Exact Comparison of Explanatory Strength of Two Dependent Predictors
The Paired Swap Permutation Test is an exact non-parametric procedure that compares explanatory power of two dependent predictors via symmetric within-subject swapping for categorical data and ECDF mapping for continuous data.
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Amortized Probabilistic Retrieval of Atmospheric CO2 from OCO-2 Spectra Using Deep Learning with Laplace Approximations and Normalizing Flows
A deep learning method amortizes probabilistic XCO2 retrieval from OCO-2 spectra via Laplace approximations and normalizing flows, trained on simulations with model errors to achieve faster inference and better-calibrated uncertainties than operational solvers.