41 citations · 198 across the 24 of their papers we have counts for
6 papers · 1 filter
A new gradient-free active subspace estimation method with application to rare event probability estimation
Valentin Breaz, Miguel Munoz Zuniga, Olivier Zahm +1
To reduce the cost of estimating the probability of a rare event involving a very large number of random parameters, we propose a new strategy for dimension reduction coupled with…
Continuous Gaussian mixture solution for linear Bayesian inversion with application to Laplace priors
Rafael Flock, Yiqiu Dong, Felipe Uribe +1
We focus on Bayesian inverse problems with Gaussian likelihood, linear forward model, and priors that can be formulated as a Gaussian mixture. Such a mixture is expressed as an int…
Principal Feature Detection via -Sobolev Inequalities
Matthew T. C. Li, Youssef Marzouk, Olivier Zahm
We investigate the approximation of high-dimensional target measures as low-dimensional updates of a dominating reference measure. This approximation class replaces the associated…
Gradient-based data and parameter dimension reduction for Bayesian models: an information theoretic perspective
Ricardo Baptista, Youssef Marzouk, Olivier Zahm
We consider the problem of reducing the dimensions of parameters and data in non-Gaussian Bayesian inference problems. Our goal is to identify an "informed" subspace of the paramet…
Data-Free Likelihood-Informed Dimension Reduction of Bayesian Inverse Problems
Tiangang Cui, Olivier Zahm
Identifying a low-dimensional informed parameter subspace offers a viable path to alleviating the dimensionality challenge in the sampled-based solution to large-scale Bayesian inv…
Greedy inference with structure-exploiting lazy maps
Michael C. Brennan, Daniele Bigoni, Olivier Zahm +2
We propose a framework for solving high-dimensional Bayesian inference problems using \emph{structure-exploiting} low-dimensional transport maps or flows. These maps are confined t…