7 citations · 13 across the 5 of their papers we have counts for
5 papers · 1 filter
A unified performance analysis of likelihood-informed subspace methods
Tiangang Cui, Xin T. Tong
The likelihood-informed subspace (LIS) method offers a viable route to reducing the dimensionality of high-dimensional probability distributions arising in Bayesian inference. LIS…
Randomized Reduced Forward Models for Efficient Metropolis--Hastings MCMC, with Application to Subsurface Fluid Flow and Capacitance Tomography
Colin Fox, Tiangang Cui, Markus Neumayer
Bayesian modelling and computational inference by Markov chain Monte Carlo (MCMC) is a principled framework for large-scale uncertainty quantification, though is limited in practic…
Optimization-Based MCMC Methods for Nonlinear Hierarchical Statistical Inverse Problems
Johnathan Bardsley, Tiangang Cui
In many hierarchical inverse problems, not only do we want to estimate high- or infinite-dimensional model parameters in the parameter-to-observable maps, but we also have to estim…
Scalable optimization-based sampling on function space
Johnathan Bardsley, Tiangang Cui, Youssef Marzouk +1
Optimization-based samplers such as randomize-then-optimize (RTO) [2] provide an efficient and parallellizable approach to solving large-scale Bayesian inverse problems. These meth…
A posteriori stochastic correction of reduced models in delayed acceptance MCMC, with application to multiphase subsurface inverse problems
Tiangang Cui, Colin Fox, Michael J O'Sullivan
Sample-based Bayesian inference provides a route to uncertainty quantification in the geosciences, and inverse problems in general, though is very computationally demanding in the…