The mutual-information measure of epistemic uncertainty is not reducible by additional data, requiring a split into aleatoric, sample-reducible epistemic, and mechanism-reducible epistemic uncertainty.
Sources of uncertainty in machine learning–a statisticians’ view
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A statistical framework decomposes human annotation outcomes into four interpretable variation sources and extends classical measurement-error models to handle both shared and individualized notions of truth.
Applies Dempster-Shafer Theory with conditional BPAs and Yager's combination rule, then variance-based sensitivity analysis, to represent and rank uncertainties in LiDAR detection for a SOTIF scenario.
citing papers explorer
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Epistemic Uncertainty Is Not the Reducible Kind
The mutual-information measure of epistemic uncertainty is not reducible by additional data, requiring a split into aleatoric, sample-reducible epistemic, and mechanism-reducible epistemic uncertainty.
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From Ground Truth to Measurement: A Statistical Framework for Human Labeling
A statistical framework decomposes human annotation outcomes into four interpretable variation sources and extends classical measurement-error models to handle both shared and individualized notions of truth.
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Uncertainty Representation in a SOTIF-Related Use Case with Dempster-Shafer Theory for LiDAR Sensor-Based Object Detection
Applies Dempster-Shafer Theory with conditional BPAs and Yager's combination rule, then variance-based sensitivity analysis, to represent and rank uncertainties in LiDAR detection for a SOTIF scenario.