83 citations · 126 across the 30 of their papers we have counts for
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Convergence Acceleration of Ensemble Kalman Inversion in Nonlinear Settings
Neil K. Chada, Xin T. Tong
Many data-science problems can be formulated as an inverse problem, where the parameters are estimated by minimizing a proper loss function. When complicated black-box models are i…
Posterior Convergence Analysis of -Stable Sheets
Neil K. Chada, Sari Lasanen, Lassi Roininen
This paper is concerned with the theoretical understanding of -stable sheets on . Our motivation for this is in the context of Bayesian inverse problems, where…
Elements of asymptotic theory with outer probability measures
Jeremie Houssineau, Neil K. Chada, Emmanuel Delande
Outer measures can be used for statistical inference in place of probability measures to bring flexibility in terms of model specification. The corresponding statistical procedures…
On the Incorporation of Box-Constraints for Ensemble Kalman Inversion
Neil K. Chada, Claudia Schillings, Simon Weissmann
The Bayesian approach to inverse problems is widely used in practice to infer unknown parameters from noisy observations. In this framework, the ensemble Kalman inversion has been…
Tikhonov Regularization Within Ensemble Kalman Inversion
Neil K. Chada, Andrew M. Stuart, Xin T. Tong
Ensemble Kalman inversion is a parallelizable methodology for solving inverse or parameter estimation problems. Although it is based on ideas from Kalman filtering, it may be viewe…