2 papers
stat.CO2020
Data-Driven Forward Discretizations for Bayesian Inversion
Daniele Bigoni, Yuming Chen, Nicolas Garcia Trillos +2
This paper suggests a framework for the learning of discretizations of expensive forward models in Bayesian inverse problems. The main idea is to incorporate the parameters governi…
stat.CO2019
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…