59 citations · 190 across the 27 of their papers we have counts for
4 papers · 1 filter
Efficient Marginalization-based MCMC Methods for Hierarchical Bayesian Inverse Problems
Arvind K. Saibaba, Johnathan Bardsley, D. Andrew Brown +1
Hierarchical models in Bayesian inverse problems are characterized by an assumed prior probability distribution for the unknown state and measurement error precision, and hyper-pri…
Subspace-based dimension reduction for chemical kinetics applications with epistemic uncertainty
M. Vohra, A. Alexanderian, H. Guy +1
We focus on an efficient approach for quantification of uncertainty in complex chemical reaction networks with a large number of uncertain parameters. Parameter dimension reduction…
Sensitivity-driven adaptive construction of reduced-space surrogates
Manav Vohra, Alen Alexanderian, Cosmin Safta +1
We develop a systematic approach for surrogate model construction in reduced input parameter spaces. A sparse set of model evaluations in the original input space is used to approx…
Goal-Oriented Optimal Design of Experiments for Large-Scale Bayesian Linear Inverse Problems
Ahmed Attia, Alen Alexanderian, Arvind K. Saibaba
We develop a framework for goal-oriented optimal design of experiments (GOODE) for large-scale Bayesian linear inverse problems governed by PDEs. This framework differs from classi…