Scalable posterior approximations for large-scale Bayesian inverse
problems via likelihood-informed parameter and state reduction

Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction

20 October 2015

Youssef M. Marzouk

Karen E. Willcox

Papers citing "Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction"

9 / 9 papers shown

Title
Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis Qiao Chen Elise Arnaud Ricardo Baptista O. Zahm 79 1 0 19 Jun 2024
Dimension-independent likelihood-informed MCMC Tiangang Cui K. Law Youssef M. Marzouk 49 200 0 13 Nov 2014
Optimal low-rank approximations of Bayesian linear inverse problems Alessio Spantini A. Solonen Tiangang Cui James Martin L. Tenorio Youssef Marzouk 73 130 0 13 Jul 2014
Likelihood-informed dimension reduction for nonlinear inverse problems Tiangang Cui James Martin Youssef M. Marzouk A. Solonen Alessio Spantini 67 159 0 19 Mar 2014
Data-Driven Model Reduction for the Bayesian Solution of Inverse Problems Tiangang Cui Youssef M. Marzouk Karen E. Willcox 48 169 0 17 Mar 2014
A computational framework for infinite-dimensional Bayesian inverse problems: Part II. Stochastic Newton MCMC with application to ice sheet flow inverse problems N. Petra James Martin G. Stadler Omar Ghattas 68 231 0 28 Aug 2013
A computational framework for infinite-dimensional Bayesian inverse problems. Part I: The linearized case, with application to global seismic inversion T. Bui-Thanh Omar Ghattas James Martin G. Stadler 71 392 0 06 Aug 2013
Optimal scaling and diffusion limits for the Langevin algorithm in high dimensions Natesh S. Pillai Andrew M. Stuart Alexandre Hoang Thiery 87 99 0 02 Mar 2011
Diffusion limits of the random walk Metropolis algorithm in high dimensions Jonathan C. Mattingly Natesh S. Pillai Andrew M. Stuart 99 114 0 22 Mar 2010