14 citations · 30 across the 12 of their papers we have counts for
5 papers · 1 filter
MALA-within-Gibbs samplers for high-dimensional distributions with sparse conditional structure
X. T. Tong, M. Morzfeld, Y. M. Marzouk
Markov chain Monte Carlo (MCMC) samplers are numerical methods for drawing samples from a given target probability distribution. We discuss one particular MCMC sampler, the MALA-wi…
Bayesian waveform-based calibration of high-pressure acoustic emission systems with ball drop measurements
Chen Gu, Ulrich Mok, Youssef M. Marzouk +4
Acoustic emission (AE) is a widely used technology to study source mechanisms and material properties during high-pressure rock failure experiments. It is important to understand t…
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…
A layered multiple importance sampling scheme for focused optimal Bayesian experimental design
Chi Feng, Youssef M. Marzouk
We develop a new computational approach for "focused" optimal Bayesian experimental design with nonlinear models, with the goal of maximizing expected information gain in targeted…
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…