Paper Citation Record · LEDGER

Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2001.06270.

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

pith.paper-citation-record.v1
2001.06270 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:10:48.330674Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T09:40:48.846263Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 39db84de-a385-47f3-a01f-3c7152b2c792 · inbound

RL-DAUNCE: Reinforcement Learning-Driven Data Assimilation with Uncertainty-Aware Constrained Ensembles cites this paper.

RL-DAUNCE: Reinforcement Learning-Driven Data Assimilation with Uncertainty-Aware Constrained Ensembles Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T23:10:48.330674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d8b9c3a-2005-4a83-87db-307a6226a973 · inbound

Pathwise Learning of Stochastic Dynamical Systems with Partial Observations cites this paper.

Pathwise Learning of Stochastic Dynamical Systems with Partial Observations Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-16T09:40:48.848191Z

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.

Observation d815301a-40e9-4de8-834c-726dc832d31d · inbound

Pathwise Learning of Stochastic Dynamical Systems with Partial Observations cites this paper.

Pathwise Learning of Stochastic Dynamical Systems with Partial Observations Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-03T06:52:54.353602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60890655-2ad0-4894-b10e-231f1fc13b15 · inbound

Learning to Trust AI and Data-driven models in Data Assimilation through a Multifidelity Ensemble Gaussian Mixture Filter Framework cites this paper.

Learning to Trust AI and Data-driven models in Data Assimilation through a Multifidelity Ensemble Gaussian Mixture Filter Framework Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:26:11.512843Z

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.

Observation 5344fd8d-a790-41b2-a3d5-f7e4cb0259ca · inbound

Learning Discriminators for Resampling in the Ensemble Gaussian Mixture Filter through a Normalizing Flow Approach cites this paper.

Learning Discriminators for Resampling in the Ensemble Gaussian Mixture Filter through a Normalizing Flow Approach Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:51:42.360103Z

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.