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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 226–250 of 309 papers

TitleStatusHype
Posterior Conformal Prediction—0
Prediction Interval Construction Method for Electricity Prices—0
Prediction intervals for neural network models using weighted asymmetric loss functions—0
Prediction Intervals and Confidence Regions for Symbolic Regression Models based on Likelihood Profiles—0
Prediction intervals for Deep Neural Networks—0
Will My Robot Achieve My Goals? Predicting the Probability that an MDP Policy Reaches a User-Specified Behavior Target—0
Prediction Intervals in the Beta Autoregressive Moving Average Model—0
A Minimax-MDP Framework with Future-imposed Conditions for Learning-augmented Problems—0
A framework for predicting, interpreting, and improving Learning Outcomes—0
Unveiling Nonlinear Dynamics in Catastrophe Bond Pricing: A Machine Learning Perspective—0
A Distribution Adaptive Framework for Prediction Interval Estimation Using Nominal Variables—0
Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case—0
Probabilistic Neural Networks (PNNs) with t-Distributed Outputs: Adaptive Prediction Intervals Beyond Gaussian Assumptions—0
Probabilistic predictions of SIS epidemics on networks based on population-level observations—0
Probabilistic Reasoning with LLMs for k-anonymity Estimation—0
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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