72 citations · 188 across the 26 of their papers we have counts for
3 papers · 1 filter
Dimension reduction via score ratio matching
Ricardo Baptista, Michael Brennan, Youssef Marzouk
Gradient-based dimension reduction decreases the cost of Bayesian inference and probabilistic modeling by identifying maximally informative (and informed) low-dimensional projectio…
An adaptive ensemble filter for heavy-tailed distributions: tuning-free inflation and localization
Mathieu Le Provost, Ricardo Baptista, Jeff D. Eldredge +1
Heavy tails is a common feature of filtering distributions that results from the nonlinear dynamical and observation processes as well as the uncertainty from physical sensors. In…
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…