1 citations · 1 across the 1 of their papers we have counts for
7 papers
Computing f-Divergences and Distances of High-Dimensional Probability Density Functions -- Low-Rank Tensor Approximations
Alexander Litvinenko, Youssef Marzouk, Hermann G. Matthies +2
Very often, in the course of uncertainty quantification tasks or data analysis, one has to deal with high-dimensional random variables (RVs). A high-dimensional RV can be described…
Coupling techniques for nonlinear ensemble filtering
Alessio Spantini, Ricardo Baptista, Youssef Marzouk
We consider filtering in high-dimensional non-Gaussian state-space models with intractable transition kernels, nonlinear and possibly chaotic dynamics, and sparse observations in s…
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…
Certified dimension reduction in nonlinear Bayesian inverse problems
Olivier Zahm, Tiangang Cui, Kody Law +2
We propose a dimension reduction technique for Bayesian inverse problems with nonlinear forward operators, non-Gaussian priors, and non-Gaussian observation noise. The likelihood f…
A Stein variational Newton method
Gianluca Detommaso, Tiangang Cui, Alessio Spantini +2
Stein variational gradient descent (SVGD) was recently proposed as a general purpose nonparametric variational inference algorithm [Liu & Wang, NIPS 2016]: it minimizes the Kullbac…
Inference via low-dimensional couplings
Alessio Spantini, Daniele Bigoni, Youssef Marzouk
We investigate the low-dimensional structure of deterministic transformations between random variables, i.e., transport maps between probability measures. In the context of statist…