This work develops a new multifidelity ensemble Kalman filter (MFEnKF) algorithm based on
a linear control variate framework. The approach allows for rigorous multifidelity extensions …

The ensemble Kalman inversion is widely used in practice to estimate unknown parameters
from noisy measurement data. Its low computational costs, straightforward implementation …

This article reviews the application of some advanced Monte Carlo techniques in the context
of multilevel Monte Carlo (MLMC). MLMC is a strategy employed to compute expectations …

The identification of parameters in mathematical models using noisy observations is a
common task in uncertainty quantification. We employ the framework of Bayesian inversion …

Multilevel ensemble-based data assimilation (DA) as an alternative to standard (single-
level) ensemble-based DA for reservoir history matching problems is considered. Restricted …

The ensemble Kalman inversion (EKI) method is a method for the estimation of unknown
parameters in the context of (Bayesian) inverse problems. The method approximates the …

Gaussian random fields are popular models for spatially varying uncertainties, arising for
instance in geotechnical engineering, hydrology or image processing. A Gaussian random …

In this paper, we consider computing expectations with respect to probability laws
associated with a certain class of stochastic systems. In order to achieve such a task, one …

Abstract Ensemble Kalman filter (EnKF) is an important data assimilation method for high-
dimensional geophysical systems. Efficient implementation of EnKF in practice often …

In this work, we show that uniform integrability is not a necessary condition for central limit
theorems (CLT) to hold for normalized multilevel Monte Carlo (MLMC) estimators and we …