Paper Citation Record · LEDGER

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning

As of 19 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2507.20379.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.20379 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:52:10.001167Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T13:15:29.708198Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

43 of 43 outbound references displayed

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External citation measurements

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation c1712378-7527-4db2-a5c6-4ae2046400a3 · outbound

This paper cites On some Lipschitz -type functions.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning On some Lipschitz -type functions

Reference 1

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Observation 6cc9b5ef-592b-4e2b-937e-10b1cd4f3391 · outbound

This paper cites Data-driven approximation of stationary nonlinear filters with optimal transport maps.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Data-driven approximation of stationary nonlinear filters with optimal transport maps

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:52:10.747758Z

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Observation c5217ff1-e706-4f62-a44b-d6371e817c81 · outbound

This paper cites Fast filtering of non-Gaussian models using Amortized Optimal Transport Maps.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Fast filtering of non-Gaussian models using Amortized Optimal Transport Maps

Reference 3

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Observation ae562cee-31d8-48be-a2a6-d306a2ab6059 · outbound

This paper cites Stability and infinitesimal robustness of posterior distribution and posterior quantities.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Stability and infinitesimal robustness of posterior distribution and posterior quantities

Reference 4

Resolution: verified exact; doi, observed 2026-08-15T17:52:10.225972Z

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Observation de18e868-6b85-4e84-aeb0-0d4bfc5f973b · outbound

This paper cites Uniform stability of posteriors.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Uniform stability of posteriors

Reference 5

Resolution: verified exact; doi, observed 2026-08-15T17:52:10.214087Z

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Observation f82ec21b-448a-4c4e-bf3f-c145fc12ec11 · outbound

This paper cites Berger and Elías Moreno.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Berger and Elías Moreno

Reference 6

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This paper cites Bayesian robustness with more than one class of contamination.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Bayesian robustness with more than one class of contamination

Reference 7

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Observation 0baa9dfe-5172-4412-a9b1-fd2fbd09d15b · outbound

This paper cites Burt, Carl Edward Rasmussen, and Mark van der Wilk.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Burt, Carl Edward Rasmussen, and Mark van der Wilk

Reference 8

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Observation ad4f8a7c-f89d-45cd-b254-05d0a2605801 · outbound

This paper cites Online variational filtering and parameter learning.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Online variational filtering and parameter learning

Reference 9

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Observation adb2ad85-36a7-45ad-bfb8-76c77c6f7981 · outbound

This paper cites Statistical Accuracy of Approximate Filtering Methods.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Statistical Accuracy of Approximate Filtering Methods

Reference 10

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Observation cb9181a0-f2fc-40f3-8d4b-c57a8eae1974 · outbound

This paper cites Measuring dependence in the Wasserstein distance for Bayesian nonparametric models.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Measuring dependence in the Wasserstein distance for Bayesian nonparametric models

Reference 11

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Observation 94b3a8ff-9786-43ea-ab02-8cd202216a1e · outbound

This paper cites Additive smoothing error in backward variational inference for general state-space models.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Additive smoothing error in backward variational inference for general state-space models

Reference 12

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Observation 6a3c9b2a-f62a-4281-bb8e-8d0fb074e32f · outbound

This paper cites A survey of convergence results on particle filtering.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning A survey of convergence results on particle filtering

Reference 13

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This paper cites Stable approximation schemes for optimal filters.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Stable approximation schemes for optimal filters

Reference 14

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Observation 65a7f272-0318-4f15-b216-e7fe0e7ad769 · outbound

This paper cites Deep composition of tensor-trains using squared inverse Rosenblatt transport.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Deep composition of tensor-trains using squared inverse Rosenblatt transport

Reference 15

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Observation c8d58f59-8685-4748-8832-f7afcabefe23 · outbound

This paper cites Memming Park.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Memming Park

Reference 16

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Observation d3fde9d7-3094-45d4-9bed-c7947b9bb6e4 · outbound

This paper cites On the performance of particle filters with adaptive number of particles.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning On the performance of particle filters with adaptive number of particles

Reference 17

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Observation b177a9eb-d28c-449c-989b-5c6931f672cf · outbound

This paper cites Sequential Monte Carlo Methods in Practice , volume 1.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Sequential Monte Carlo Methods in Practice , volume 1

Reference 18

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Observation e306bf7e-be97-433f-b5cf-cf6d23c678cf · outbound

This paper cites Bayesian Posterior Perturbation Analysis with Integral Probability Metrics.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Bayesian Posterior Perturbation Analysis with Integral Probability Metrics

Reference 19

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This paper cites Gibbs and Su Francis Edward.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Gibbs and Su Francis Edward

Reference 20

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This paper cites Transport map coupling filter for state-parameter estimation.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Transport map coupling filter for state-parameter estimation

Reference 21

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Observation 5ba45d80-fbac-4671-b84b-b12805dc2d8d · outbound

This paper cites Gustafson and Wasserman Larry.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Gustafson and Wasserman Larry

Reference 22

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This paper cites Gaussian filters for nonlinear filtering problems.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Gaussian filters for nonlinear filtering problems

Reference 23

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A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning On the approximation accuracy of gaussian variational inference

Reference 24

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This paper cites Data Assimilation: A Mathematical Introduction, volume 62.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Data Assimilation: A Mathematical Introduction, volume 62

Reference 25

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Observation 6b7de786-65e4-4647-9425-9e01b5fc36f2 · outbound

This paper cites Stability and uniform approximation of nonlinear filters using the Hilbert metric and application to particle filters.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Stability and uniform approximation of nonlinear filters using the Hilbert metric and application to particle filters

Reference 26

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Observation 3ce8f76b-d9bc-4d2f-9173-10aed27385ee · outbound

This paper cites Large Sample Asymptotics for the Ensemble Kalman Filter.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Large Sample Asymptotics for the Ensemble Kalman Filter

Reference 27

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A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Unresolved cited work

Reference 28

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Observation d1478498-66b9-4b59-a244-f9dc506ab22f · outbound

This paper cites Probabilistic filter and smoother for variational inference of Bayesian linear dynamical system.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Probabilistic filter and smoother for variational inference of Bayesian linear dynamical system

Reference 29

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A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Unresolved cited work

Reference 30

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Observation 8f61f7f7-79ee-4a8e-a436-406d3aefc70a · outbound

This paper cites Can local particle filters beat the curse of dimensionality? Annals of Applied Probability, 25 0 (5): 0 2809--2866, 2015.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Can local particle filters beat the curse of dimensionality? Annals of Applied Probability, 25 0 (5): 0 2809--2866, 2015

Reference 31

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Observation 72e1cfc2-eeb7-48fc-8c8a-4d16bf0632ee · outbound

This paper cites Wasserstein convergence in Bayesian and frequentist deconvolution models.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Wasserstein convergence in Bayesian and frequentist deconvolution models

Reference 32

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Observation 235c6779-6c9d-4220-aa69-c17bdc5e4270 · outbound

This paper cites Posterior ranges of functions of parameters under priors with specified quantiles.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Posterior ranges of functions of parameters under priors with specified quantiles

Reference 33

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Observation 19bbc8bf-f841-4251-9c8f-ea643b28860f · outbound

This paper cites Sanz-Alonso, A.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Sanz-Alonso, A

Reference 34

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Observation 6a68517c-8c33-49cc-a9ba-1be827d58258 · outbound

This paper cites Measuring local sensitivity in Bayesian inference using a new class of metrics.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Measuring local sensitivity in Bayesian inference using a new class of metrics

Reference 35

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This paper cites Bounds on posterior expectation for density bounded classes with constant bandwith.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Bounds on posterior expectation for density bounded classes with constant bandwith

Reference 36

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Observation 1d3bb954-670d-40d8-86c9-f46584ba7305 · outbound

This paper cites On the local lipschitz stability of Bayesian inverse problems.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning On the local lipschitz stability of Bayesian inverse problems

Reference 37

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This paper cites Bayesian Filtering and Smoothing.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Bayesian Filtering and Smoothing

Reference 38

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Observation 84b8528e-e335-424c-8d37-99573d08d389 · outbound

This paper cites Optimal Transport: Old and New.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Optimal Transport: Old and New

Reference 39

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Observation cb2366f0-af07-4723-9cec-9dc8d96e09dc · outbound

This paper cites Factorization-based online variational inference for state-parameter estimation of partially observable nonlinear dynamical systems.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Factorization-based online variational inference for state-parameter estimation of partially observable nonlinear dynamical systems

Reference 40

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This paper cites Optimal transport and Wasserstein distance.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Optimal transport and Wasserstein distance

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:52:10.601895Z

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Observation 94742320-25bf-40e8-81fd-b2bd07bd8838 · outbound

This paper cites Convergence rates of variational posterior distributions.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Convergence rates of variational posterior distributions

Reference 42

Resolution: verified exact; doi, observed 2026-08-15T17:52:10.034616Z

Source-reported events for the cited work

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Observation 7e665dc4-16d3-4803-8de1-371a09e0ed4f · outbound

This paper cites Tensor-train methods for sequential state and parameter learning in state-space models.

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning Tensor-train methods for sequential state and parameter learning in state-space models

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:52:10.589773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation f810c88b-f622-4584-a633-760c5b6b9d89 · inbound

Sequential Bayesian parameter-state estimation in dynamical systems with noisy and incomplete observations via a variational framework cites this paper.

Sequential Bayesian parameter-state estimation in dynamical systems with noisy and incomplete observations via a variational framework A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-03T13:18:33.489972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.