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Prediction Intervals

A prediction interval is an estimate of an interval in which a future observation will fall, with a certain probability, given what has already been observed. Prediction intervals are often used in regression analysis.

Papers

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Showing 241–250 of 309 papers

TitleStatusHype
Pseudo-Observations and Super Learner for the Estimation of the Restricted Mean Survival Time—0
Quantile Extreme Gradient Boosting for Uncertainty Quantification—0
Uncertainty measurement for complex event prediction in safety-critical systems—0
Conformal Inference of Individual Treatment Effects Using Conditional Density Estimates—0
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks—0
Zadeh's Type-2 Fuzzy Logic Systems: Precision and High-Quality Prediction Intervals—0
A Deep Generative Model Imitating Predictive Coding in Human Brain—0
Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback—0
A Data Envelopment Analysis Approach for Assessing Fairness in Resource Allocation: Application to Kidney Exchange Programs—0
Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning—0
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