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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 251–275 of 309 papers

TitleStatusHype
Conditional Coverage Estimation for High-quality Prediction Intervals—0
Disease Momentum: Estimating the Reproduction Number in the Presence of Superspreading—0
Learning Prediction Intervals for Model Performance—0
Forecast with Forecasts: Diversity Matters—0
Tolerance and Prediction Intervals for Non-normal Models—0
Marginalised Spectral Mixture Kernels with Nested Sampling—0
A Deep Generative Model Imitating Predictive Coding in Human Brain—0
Prediction intervals for Deep Neural Networks—0
Machine learning for recovery factor estimation of an oil reservoir: a tool for de-risking at a hydrocarbon asset evaluation—0
A framework for predicting, interpreting, and improving Learning Outcomes—0
Explainable boosted linear regression for time series forecasting—0
Ridge Regression Revisited: Debiasing, Thresholding and Bootstrap—0
Moment Multicalibration for Uncertainty Estimation—0
CD-split and HPD-split: efficient conformal regions in high dimensions—0
Conformal Prediction Intervals for Neural Networks Using Cross Validation—0
Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN—0
AutoCP: Automated Pipelines for Accurate Prediction Intervals—0
Calibrated Reliable Regression using Maximum Mean Discrepancy—0
Statistical Verification of Autonomous Systems using Surrogate Models and Conformal Inference—0
Online Control of the False Coverage Rate and False Sign Rate—0
Optimal Uncertainty-guided Neural Network Training—0
Per-sample Prediction Intervals for Extreme Learning Machines—0
A Unified Framework for Random Forest Prediction Error EstimationCode0
Uncertainty Quantification in Ensembles of Honest Regression Trees using Generalized Fiducial Inference—0
Learn-By-Calibrating: Using Calibration as a Training Objective—0
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