7 papers
Statistical accuracy of the ensemble Kalman filter in the near-linear setting
E. Calvello, J. A. Carrillo, F. Hoffmann +3
Estimating the state of a dynamical system from partial and noisy observations is a ubiquitous problem in a large number of applications, such as probabilistic weather forecasting…
Accuracy of the Ensemble Kalman Filter in the Near-Linear Setting
Edoardo Calvello, Pierre Monmarché, Andrew M. Stuart +1
The filtering distribution captures the statistics of the state of a dynamical system from partial and noisy observations. Classical particle filters provably approximate this dist…
Statistical Accuracy of Approximate Filtering Methods
J. A. Carrillo, F. Hoffmann, A. M. Stuart +1
Estimating the statistics of the state of a dynamical system, from partial and noisy observations, is both mathematically challenging and finds wide application. Furthermore, the a…
The Mean Field Ensemble Kalman Filter: Near-Gaussian Setting
J. A. Carrillo, F. Hoffmann, A. M. Stuart +1
The ensemble Kalman filter is widely used in applications because, for high dimensional filtering problems, it has a robustness that is not shared for example by the particle filte…
Consensus-based Optimization and Ensemble Kalman Inversion for Global Optimization Problems with Constraints
J. A. Carrillo, C. Totzeck, U. Vaes
We introduce a practical method for incorporating equality and inequality constraints in global optimization methods based on stochastic interacting particle systems, specifically…
Consensus Based Sampling
J. A. Carrillo, F. Hoffmann, A. M. Stuart +1
We propose a novel method for sampling and optimization tasks based on a stochastic interacting particle system. We explain how this method can be used for the following two goals:…
Derivative-free Bayesian Inversion Using Multiscale Dynamics
G. A. Pavliotis, A. M. Stuart, U. Vaes
Inverse problems are ubiquitous because they formalize the integration of data with mathematical models. In many scientific applications the forward model is expensive to evaluate,…