186 citations · 711 across the 31 of their papers we have counts for
4 papers · 1 filter
Dimension-independent likelihood-informed MCMC
Tiangang Cui, Kody J. H. Law, Youssef M. Marzouk
Many Bayesian inference problems require exploring the posterior distribution of high-dimensional parameters that represent the discretization of an underlying function. This work…
Optimal low-rank approximations of Bayesian linear inverse problems
Alessio Spantini, Antti Solonen, Tiangang Cui +3
In the Bayesian approach to inverse problems, data are often informative, relative to the prior, only on a low-dimensional subspace of the parameter space. Significant computationa…
Likelihood-informed dimension reduction for nonlinear inverse problems
Tiangang Cui, James Martin, Youssef M. Marzouk +2
The intrinsic dimensionality of an inverse problem is affected by prior information, the accuracy and number of observations, and the smoothing properties of the forward operator.…
Data-Driven Model Reduction for the Bayesian Solution of Inverse Problems
Tiangang Cui, Youssef M. Marzouk, Karen E. Willcox
One of the major challenges in the Bayesian solution of inverse problems governed by partial differential equations (PDEs) is the computational cost of repeatedly evaluating numeri…