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.
URL https://projecteuclid.org/journals/annals-of-statistics/ volume-49/issue-1/Predictive-inference-with-the-jackknife/10
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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.