447 citations · 2.5k across the 63 of their papers we have counts for
6 papers · 1 filter
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…
Deterministic Mean-field Ensemble Kalman Filtering
Kody J. H. Law, Hamidou Tembine, Raul Tempone
The proof of convergence of the standard ensemble Kalman filter (EnKF) from Legland etal. (2011) is extended to non-Gaussian state space models. A density-based deterministic appro…
Well-Posedness And Accuracy Of The Ensemble Kalman Filter In Discrete And Continuous Time
D. T. B. Kelly, K. J. H. Law, A. M. Stuart
The ensemble Kalman filter (EnKF) is a method for combining a dynamical model with data in a sequential fashion. Despite its widespread use, there has been little analysis of its t…
MAP Estimators and Their Consistency in Bayesian Nonparametric Inverse Problems
Masoumeh Dashti, Kody J. H. Law, Andrew M. Stuart +1
We consider the inverse problem of estimating an unknown function from noisy measurements of a known, possibly nonlinear, map applied to . We adopt a Bayes…
Determining White Noise Forcing From Eulerian Observations in the Navier Stokes Equation
Viet Ha Hoang, Kody J. H. Law, Andrew M. Stuart
The Bayesian approach to inverse problems is of paramount importance in quantifying uncertainty about the input to and the state of a system of interest given noisy observations. H…
Accuracy and Stability of The Continuous-Time 3DVAR Filter for The Navier-Stokes Equation
D. Bloemker, K. J. H. Law, A. M. Stuart +1
The 3DVAR filter is prototypical of methods used to combine observed data with a dynamical system, online, in order to improve estimation of the state of the system. Such methods a…