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Paper Citation Record · LEDGER

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty

As of 16 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2411.18864.

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pith.paper-citation-record.v1
2411.18864 v1

Coverage vector

measured 38 of 38 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

38 of 38 outbound references displayed

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External citation measurements

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Outbound references

Observation 8e32ec91-6c48-4d06-9092-c16fef537eec · outbound

This paper cites Neural assimilation.

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Neural assimilation

Reference 1

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This paper cites SIAM, 2016.

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty SIAM, 2016

Reference 2

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This paper cites Representing parametric probabilistic models tainted with imprecision.

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Representing parametric probabilistic models tainted with imprecision

Reference 3

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Observation 5c6f74bb-2ec2-425b-9fc0-9cf574112e98 · outbound

This paper cites Robust Kalman and Bayesian set-valued filtering and model validation for linear stochastic systems.

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Robust Kalman and Bayesian set-valued filtering and model validation for linear stochastic systems

Reference 4

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Observation 1a2b311b-3565-4b57-9cc7-ecf7fe2d6fc5 · outbound

This paper cites Analysis scheme in the ensemble Kalman filter.

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Analysis scheme in the ensemble Kalman filter

Reference 5

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Observation 406dd1e1-27ad-4b68-ac67-6c876c8a5d46 · outbound

This paper cites Observer control for bearings-only tracking using possibility functions.

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Observer control for bearings-only tracking using possibility functions

Reference 6

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Observation 91c3b7f5-5607-462d-af61-90d5d062e7b5 · outbound

This paper cites Reducing the bandwidth of sparse symmetric matrices.

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Reducing the bandwidth of sparse symmetric matrices

Reference 7

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Observation 8eba5afa-5305-42a4-8fda-da59a1a636f7 · outbound

This paper cites Conditioning in possibility theory with strict order norms.

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Conditioning in possibility theory with strict order norms

Reference 8

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Observation cb556002-c4f1-4216-8d4c-c639e3fadf99 · outbound

This paper cites The Dempster–Shafer calculus for statisticians.

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty The Dempster–Shafer calculus for statisticians

Reference 9

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This paper cites Possibility theory, probability and fuzzy sets misun- derstandings, bridges and gaps: Misunderstandings, bridges and gaps.

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Possibility theory, probability and fuzzy sets misun- derstandings, bridges and gaps: Misunderstandings, bridges and gaps

Reference 10

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Observation 3f5296f0-e0d1-4d54-82f4-aa0de36b8751 · outbound

This paper cites Possibility theory and its applications: Where do we stand? Springer Handbook of Computational Intelligence , pages 31–60, 2015.

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Possibility theory and its applications: Where do we stand? Springer Handbook of Computational Intelligence , pages 31–60, 2015

Reference 11

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Observation 8c56cb49-c61e-4353-86cc-3e19e5812b66 · outbound

This paper cites Particle filters and data assimilation.Annual Review of Statistics and Its Application , 5:421–449, 2018.

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Particle filters and data assimilation.Annual Review of Statistics and Its Application , 5:421–449, 2018

Reference 12

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty The fiducial argument in statistical inference

Reference 13

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This paper cites Ensemble Kalman filtering and generalizations.

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Ensemble Kalman filtering and generalizations

Reference 14

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Observation d386dd82-748f-462d-9d07-e4b3ba6ccd57 · outbound

This paper cites A quasi-Monte Carlo method for optimal control under uncertainty.SIAM/ASA Journal on Uncertainty Quantification, 9(2):354–383, 2021.

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty A quasi-Monte Carlo method for optimal control under uncertainty.SIAM/ASA Journal on Uncertainty Quantification, 9(2):354–383, 2021

Reference 15

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Generalized fiducial inference: A review and new results

Reference 16

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Parameter estimation with a class of outer probability measures

Reference 17

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This paper cites A linear algorithm for multi-target tracking in the context of possibility theory.

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty A linear algorithm for multi-target tracking in the context of possibility theory

Reference 18

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Smoothing and filtering with a class of outer measures

Reference 19

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Elements of asymptotic theory with outer probability measures

Reference 20

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty On domain lo- calization in ensemble-based Kalman filter algorithms

Reference 21

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Unresolved cited work

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Atmospheric modeling, data assimilation and predictability

Reference 23

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Understanding the ensemble Kalman filter

Reference 24

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Ensemble Kalman filtering with an alternative representation of uncertainty

Reference 25

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Monte Carlo and quasi-Monte Carlo sampling , volume 20

Reference 26

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Reasoning with belief functions: An analysis of compatibility

Reference 27

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Localization in the ensemble Kalman filter

Reference 28

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Probabilistic forecasting and Bayesian data assimilation

Reference 29

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Monte Carlo statistical methods , volume 2

Reference 30

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Cambridge University Press, 2023

Reference 31

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty An optimal transport formulation of the ensemble Kalman filter

Reference 32

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Nonlinear measurement function in the ensemble Kalman filter

Reference 33

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Extended Kalman filter tutorial

Reference 34

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Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Ensemble square root filters

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T10:57:38.449683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T10:57:38.321553Z digest=sha256:0e02a254917ec398687a7b70028d215fff47f3da03d024bdec9f8a568334ab32

Observation ed1a6593-69ed-4976-80ce-704dbfa3ca13 · outbound

This paper cites Nonlinear data assimilation for high- dimensional systems: -with geophysical applications.

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Nonlinear data assimilation for high- dimensional systems: -with geophysical applications

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T10:57:38.430013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T10:57:38.325995Z digest=sha256:ad9ddc7fdda297c90d5b5026c2cfc674ae930dea326a698c84418d8e9c245b67

Observation 64e8d3d9-e370-44c0-b85a-1bd8f448e392 · outbound

This paper cites The unscented Kalman filter for nonlinear estimation.

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty The unscented Kalman filter for nonlinear estimation

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T10:57:38.412652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T10:57:38.330073Z digest=sha256:54d6241032030c1bce011e02702be3806b86dc562d9541ea67039fd0cc57c359

Observation 510fde54-0c6d-496e-8cb2-aa12f999dfe9 · outbound

This paper cites Ensemble data assimilation without perturbed observations.

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty Ensemble data assimilation without perturbed observations

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T10:57:38.398171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T10:57:38.333926Z digest=sha256:0328e09a46c3b51a7cb27cba56d49ed415bbd6136c56ed18234dbb9cc41ca2bf

Pith citing papers

No inbound Pith citation observations are available.