Related papers: Reply to "Comment on 'Ensemble Kalman filter with …
We respond to the comment arXiv:1809.03931 put forward by Li-Han Chen, Zhen Liu, and Jian-Tin Zheng on our work Kiesel, Platt, and Thomale, Phys. Rev. Lett. 110, 126405 (2013). All misunderstandings and misconceptions communicated in the…
We clarify the relationship between the conclusions of the previous Comment of A. Helfer [1] and that of our Brief Report [arXiv:0906.5315]
The ensemble Kalman filter (EnKF) is a popular technique for performing inference in state-space models (SSMs), particularly when the dynamic process is high-dimensional. Unlike reweighting methods such as sequential Monte Carlo (SMC, i.e.…
We compare the results of our two papers with the results of the paper Aratyn H., Gomes J.F., Zimerman A.H., Higher order Painlev\'e equations and their symmetries via reductions of a class of integrable models, J. Phys. A: Math. Theor., V.…
Among the class of nonlinear particle filtering methods, the Ensemble Kalman Filter (EnKF) has gained recent attention for its use in solving inverse problems. We review the original method and discuss recent developments in particular in…
We use indecomposable ultrafilters to answer some questions of Hayut, Karagila paper "Spectra of uniformity". It is shown that the bound on the strength by T. Usuba "A note on uniform ultrafilters in choiceless context" is optimal.
The unscented Kalman filter (UKF) is a commonly used algorithm capable of estimating the states of nonlinear dynamic systems. It carefully chooses a set of sample points, called sigma points that capture the nonlinear system states…
In this manuscript we reply to the criticisms of our paper [Eur. Phys. J. A 24, 437 (2005)] raised in a recent preprint by Achasov and Kiselev [hep-ph/0606268] and demonstrate that all their criticisms are completely irrelevant and…
This is a reply to the comment from Patrick Bruno (arXiv:1211.4792) on our paper (Phys. Rev. Lett. 109, 163001 (2012)).
Comment on paper by Blanchette and Zhang, Phys. Rev. Lett. 102, 144501 (2009).
Variational inference (VI) combined with Bayesian nonlinear filtering produces state-of-the-art results for latent time-series modeling. A body of recent work has focused on sequential Monte Carlo (SMC) and its variants, e.g., forward…
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 theoretical properties. Many of the algorithmic innovations…
In this article, we present a structured Kalman filter associated with the transformation matrix for observable Kalman canonical decomposition from conventional Kalman filter (CKF) in order to generate a more accurate time scale. The…
In \cite{HK}, Hayut and Karagila asked some questions about uniform ultrafilters in a choiceless context. We provide several answers to their questions.
The ensemble Kalman filter (EnKF) is a Monte Carlo based implementation of the Kalman filter (KF) for extremely high-dimensional, possibly nonlinear and non-Gaussian state estimation problems. Its ability to handle state dimensions in the…
In this article, we consider the implications of unobservable subspaces in the construction of a Kalman filter. In particular, we consider dynamical systems which are invariant with respect to a group action, and which are therefore…
Reply to the Comment on "Unified Formulism of Andreev Reflection at a Ferromagnetic/Superconductor Interface" by Eschrig et al
A response to commenter Ke Lan's comment on our paper published in Nature Communications (2023)14:5782 by J. Yan et al
Common filters are usually based on the linear approximation of the optimal minimum mean square error estimator. The Extended and Unscented Kalman Filters handle nonlinearity through linearization and unscented transformation, respectively,…
We reply to the comment cond-mat/0008098 by Yang, Sun, and Chang on our paper "Theory of Diluted Magnetic Semiconductor Ferromagnetism", Phys. Rev. Lett. 84, 5628 (2000).