# A Stochastic Covariance Shrinkage Approach to Particle Rejuvenation in the Ensemble Transform Particle Filter

@article{Popov2021ASC, title={A Stochastic Covariance Shrinkage Approach to Particle Rejuvenation in the Ensemble Transform Particle Filter}, author={Andrey A Popov and Amit N. Subrahmanya and Adrian Sandu}, journal={ArXiv}, year={2021}, volume={abs/2109.09673} }

Abstract. Rejuvenation in particle filters is necessary to prevent the collapse of the weights when the number of particles is insufficient to sample the high probability regions of the state space. Rejuvenation is often implemented in a heuristic manner by the addition of stochastic samples that widen the support of the ensemble. This work aims at improving canonical rejuvenation methodology by the introduction of additional prior information obtained from climatological samples; the dynamical… Expand

#### One Citation

#### References

SHOWING 1-10 OF 40 REFERENCES

A Hybrid Ensemble Transform Particle Filter for Nonlinear and Spatially Extended Dynamical Systems

- Computer Science, Mathematics
- SIAM/ASA J. Uncertain. Quantification
- 2016

A hybrid filter is proposed that allows one to adaptively bridge between ensemble Kalman and particle filters and shows how to implement the concept of localization into a hybrid filter, which is key to its applicability to spatially extended systems. Expand

Second-order Accurate Ensemble Transform Particle Filters

- Computer Science, Mathematics
- SIAM J. Sci. Comput.
- 2017

This work develops second-order accurate extensions of the ETPF that allow one in particular to replace the exact solution of a linear transport problem by its Sinkhorn approximation and demonstrates that the nonlinear ensemble transform filter (NETF) arises as a special case of the general framework. Expand

Ensemble Kalman filter implementations based on shrinkage covariance matrix estimation

- Mathematics
- Ocean Dynamics
- 2015

This paper develops efficient ensemble Kalman filter (EnKF) implementations based on shrinkage covariance estimation. The forecast ensemble members at each step are used to estimate the background… Expand

A Nonparametric Ensemble Transform Method for Bayesian Inference

- Mathematics, Computer Science
- SIAM J. Sci. Comput.
- 2013

This paper proposes another transform method, which does not rely on any a priori assumptions on the underlying prior and posterior distributions and is based on solving an optimal transportation problem for discrete random variables. Expand

A parallel ensemble Kalman filter implementation based on modified Cholesky decomposition

- Computer Science
- ScalA '15
- 2015

The proposed implementation outperforms in terms of accuracy the well-known local ensemble transform Kalman filter (LETKF) for all the model variables and for the largest number of processors, is 400 times faster than the serial version of the proposed method. Expand

An Ensemble Kalman Filter Implementation Based on Modified Cholesky Decomposition for Inverse Covariance Matrix Estimation

- Mathematics, Computer Science
- SIAM J. Sci. Comput.
- 2018

The results reveal that the use of modified Cholesky for inverse covariance matrix estimation can reduce the impact of spurious correlations during the assimilation cycle, i.e., the results of the proposed method are of better quality than those obtained via the LETKF in terms of root mean square error. Expand

Analysis Scheme in the Ensemble Kalman Filter

- Mathematics
- 1998

This paper discusses an important issue related to the implementation and interpretation of the analysis scheme in the ensemble Kalman filter. It is shown that the observations must be treated as… Expand

An Explicit Probabilistic Derivation of Inflation in a Scalar Ensemble Kalman Filter for Finite Step, Finite Ensemble Convergence

- Mathematics, Computer Science
- ArXiv
- 2020

The bare-bones Scalar Pedagogical Ensemble Kalman Filter is introduced and it is shown that in the asymptotic case of ensemble size, the expected value of both the analysis mean and variance estimate of the SPEnKF converges to that of the true Kalman filter, and that the variances of both tend towards zero, at each time moment. Expand

An efficient implementation of the ensemble Kalman filter based on an iterative Sherman–Morrison formula

- Computer Science
- Stat. Comput.
- 2015

We present a practical implementation of the ensemble Kalman filter (EnKF) based on an iterative Sherman–Morrison formula. The new direct method exploits the special structure of the… Expand

An Ensemble Adjustment Kalman Filter for Data Assimilation

- Mathematics
- 2001

Abstract A theory for estimating the probability distribution of the state of a model given a set of observations exists. This nonlinear filtering theory unifies the data assimilation and ensemble… Expand