Uncertainty Quantification

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Uncertainty quantification addresses the gap between a model’s output and how reliable that output actually is. A system can produce a clear answer even when the situation itself is unclear, which can hide real risk. Some uncertainty comes from noisy or ambiguous inputs, while other uncertainty comes from gaps in what the model has learned.

Knowing this uncertainty helps guide safer decisions. When uncertainty is high, a system might slow down or avoid acting altogether. What counts as acceptable risk depends on the setting, since a wrong answer can have very different consequences across applications. Because uncertainty estimates can themselves be unreliable, teams test them under stress and changing conditions. This helps ensure the system signals doubt when it truly should.

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