For many stochastic models of interest in systems biology, such as those describing
biochemical reaction networks, exact quantification of parameter uncertainty through …

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 …

This work embeds a multilevel Monte Carlo sampling strategy into the Monte Carlo step of
the ensemble Kalman filter (EnKF) in the setting of finite dimensional signal evolution and …

In this paper the filtering of partially observed diffusions, with discrete-time observations, is
considered. It is assumed that only biased approximations of the diffusion can be obtained …

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 …

In this paper we address the problem of the prohibitively large computational cost of existing
Markov chain Monte Carlo methods for large-scale applications with high-dimensional …

In this article we develop a new sequential Monte Carlo method for multilevel Monte Carlo
estimation. In particular, the method can be used to estimate expectations with respect to a …

We design and analyse the performance of a multilevel ensemble Kalman filter method
(MLEnKF) for filtering settings where the underlying state-space model is an infinite …

We introduce a new multilevel ensemble Kalman filter method (MLEnKF) which consists of a
hierarchy of independent samples of ensemble Kalman filters (EnKF). This new MLEnKF …

We present an efficient data assimilation framework for nonlinear dynamical systems that
uses multi-fidelity statistical estimates based on a full-order model and a projection-based …