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A Bayesian Model Updating Framework for Systems Under Hybrid Uncertainties via Probability Integral Transform and Maximum Mean Discrepancy
Model updating under hybrid uncertainty is challenging because aleatory input variability makes the simulator output a probability distribution rather than a scalar, rendering the likelihood analytically intractable. Existing Approximate Bayesian Computation (ABC) methods typically employ nested Mon
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