Empirical comparison of ten BNN inference methods shows test log-likelihood can mislead on uncertainty quality and that posterior-structure innovations do not necessarily yield high-quality approximations.
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Compares Delta method, Bayesian Monte Carlo Dropout, Bootstrap, Lower-Upper Bound Estimation, and Mean-Variance Estimation for prediction intervals on turbine gas temperature data using coverage probability, normalized mean prediction interval width, and coverage width-based criterion.
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Quality of Uncertainty Quantification for Bayesian Neural Network Inference
Empirical comparison of ten BNN inference methods shows test log-likelihood can mislead on uncertainty quality and that posterior-structure innovations do not necessarily yield high-quality approximations.
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Benchmarking Machine Learning Uncertainty Quantification Methodologies for Predicting Turbine Gas Temperature Degradation
Compares Delta method, Bayesian Monte Carlo Dropout, Bootstrap, Lower-Upper Bound Estimation, and Mean-Variance Estimation for prediction intervals on turbine gas temperature data using coverage probability, normalized mean prediction interval width, and coverage width-based criterion.