5 citations · 8 across the 9 of their papers we have counts for
5 papers · 1 filter
LazyDINO: Fast, scalable, and efficiently amortized Bayesian inversion via structure-exploiting and surrogate-driven measure transport
Lianghao Cao, Joshua Chen, Michael Brennan +3
We present LazyDINO, a transport map variational inference method for fast, scalable, and efficiently amortized solutions of high-dimensional nonlinear Bayesian inverse problems wi…
Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier maps
Ricardo Baptista, Aram-Alexandre Pooladian, Michael Brennan +2
Conditional simulation is a fundamental task in statistical modeling: Generate samples from the conditionals given finitely many data points from a joint distribution. One promisin…
Expected information gain estimation via density approximations: Sample allocation and dimension reduction
Fengyi Li, Ricardo Baptista, Youssef Marzouk
Computing expected information gain (EIG) from prior to posterior (equivalently, mutual information between candidate observations and model parameters or other quantities of inter…
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…
Preserving linear invariants in ensemble filtering methods
Mathieu Le Provost, Jan Glaubitz, Youssef Marzouk
Data assimilation combines dynamical models with observations to improve state estimates. Ensemble filters sequentially assimilate observations by updating a set of samples over ti…