Deep Composition of Tensor-Trains Using Squared Inverse Rosenblatt Transports von Cui, Tiangang, Dolgov, Sergey

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Certified dimension reduction in nonlinear Bayesian inverse problems von Zahm, Olivier, Cui, Tiangang, Law, Kody, Spantini, Alessio, Marzouk, Youssef

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Semivariogram methods for modeling Whittle-Matérn priors in Bayesian inverse problems von Brown, Richard D, Bardsley, Johnathan M, Cui, Tiangang

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Identification of community structure-based brain states and transitions using functional MRI von Bian, Lingbin, Cui, Tiangang, Thomas Yeo, B.T., Fornito, Alex, Razi, Adeel, Keith, Jonathan

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A non-linear reverse-engineering method for inferring genetic regulatory networks von Wu, Siyuan, Cui, Tiangang, Zhang, Xinan, Tian, Tianhai

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Design of a Rainfall Sensor for Mobile Offshore Platform von Cui, Tiangang, Wang, Ni, Zhang, Yun, Sun, Jia, Chu, Weixian, Liu, Tao

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Data-free likelihood-informed dimension reduction of Bayesian inverse problems von Cui, Tiangang, Zahm, Olivier

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Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction von Cui, Tiangang, Dolgov, Sergey, Zahm, Olivier

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Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction von Cui, Tiangang, Marzouk, Youssef, Willcox, Karen

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A unified performance analysis of likelihood-informed subspace methods von Cui, Tiangang, Tong, Xin T.

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Multifidelity importance sampling von Peherstorfer, Benjamin, Cui, Tiangang, Marzouk, Youssef, Willcox, Karen

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Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events von Cui, Tiangang, Dolgov, Sergey, Scheichl, Robert

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DEEP IMPORTANCE SAMPLING USING TENSOR TRAINS WITH APPLICATION TO A PRIORI AND A POSTERIORI RARE EVENTS von Cui, Tiangang, Dolgov, Sergey, Scheichl, Robert

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Multilevel dimension-independent likelihood-informed MCMC for large-scale inverse problems von Cui, Tiangang, Detommaso, Gianluca, Scheichl, Robert

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Dimension-independent likelihood-informed MCMC von Cui, Tiangang, Law, Kody J.H., Marzouk, Youssef M.

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Data-driven model reduction for the Bayesian solution of inverse problems von Cui, Tiangang, Marzouk, Youssef M., Willcox, Karen E.

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Optimal Low-rank Approximations of Bayesian Linear Inverse Problems von Spantini, Alessio, Solonen, Antti, Cui, Tiangang, Martin, James, Tenorio, Luis, Marzouk, Youssef

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A posteriori stochastic correction of reduced models in delayed‐acceptance MCMC, with application to multiphase subsurface inverse problems von Cui, Tiangang, Fox, Colin, O'Sullivan, Michael J.

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Prior normalization for certified likelihood-informed subspace detection of Bayesian inverse problems von Cui, Tiangang, Tong, Xin T., Zahm, Olivier

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Scalable Optimization-Based Sampling on Function Space von Bardsley, Johnathan M., Cui, Tiangang, Marzouk, Youssef M., Wang, Zheng

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