Derives non-asymptotic error bounds for standard, defensive, and self-normalized importance sampling with random KDE proposals from geometrically ergodic Markov chains, separating n^{-1/2} Monte Carlo error from MIAE/MISE proposal error.
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A multivariate active learning approach for polynomial chaos expansion selects samples by aggregated output variance to improve surrogate accuracy and stability for vector-valued engineering responses.
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Uncertainty Quantification of Engineering Structures by Polynomial Chaos Expansion and Multivariate Active Learning
A multivariate active learning approach for polynomial chaos expansion selects samples by aggregated output variance to improve surrogate accuracy and stability for vector-valued engineering responses.