A framework for refining probabilistic zonotope disturbance models from trajectory data to compute less conservative reachable sets for linear systems with mixed bounded and Gaussian uncertainties.
Aleatory or epistemic? does it matter?
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Derives a posterior-predictive variance decomposition separating epistemic and aleatoric uncertainty in heteroscedastic Bayesian neural network models for wind power forecasting, with a dedicated validation framework tested on synthetic and real SCADA data.
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Reachability Analysis With Probabilistic Zonotopes: Learning Realized Disturbances and Refining Aleatory Uncertainty
A framework for refining probabilistic zonotope disturbance models from trajectory data to compute less conservative reachable sets for linear systems with mixed bounded and Gaussian uncertainties.
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A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting
Derives a posterior-predictive variance decomposition separating epistemic and aleatoric uncertainty in heteroscedastic Bayesian neural network models for wind power forecasting, with a dedicated validation framework tested on synthetic and real SCADA data.