Laplace approximation framework for quantile regression with mixed-effects and Gaussian processes using Fisher information and population curvature of expected loss instead of observed Hessian.
Journal of agricultural, biological and environmental Statistics , volume=
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A recursive cubing framework identifies stable hyperparameter regions for MC dropout uncertainty quantification in spatial deep learning and produces competitive or superior predictive intervals versus a statistical baseline on simulations and land-surface temperature data.
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Laplace Approximations for Mixed-Effects and Gaussian Process Quantile Regression
Laplace approximation framework for quantile regression with mixed-effects and Gaussian processes using Fisher information and population curvature of expected loss instead of observed Hessian.
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A Cubing Strategy for Identifying Stable Hyperparameter Regions for Uncertainty Quantification in Spatial Deep Learning
A recursive cubing framework identifies stable hyperparameter regions for MC dropout uncertainty quantification in spatial deep learning and produces competitive or superior predictive intervals versus a statistical baseline on simulations and land-surface temperature data.