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Paper Citation Record · LEDGER

Non-Gaussian Process Regression

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2209.03117.

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

pith.paper-citation-record.v1
2209.03117 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:15:12.161304Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T13:15:15.882224Z

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 a1d6f957-7116-4e86-b3f4-d39e8b3281ba · inbound

Bayesian Non-Parametric Inference for L\'evy Measures in State-Space Models cites this paper.

Bayesian Non-Parametric Inference for L\'evy Measures in State-Space Models Non-Gaussian Process Regression

Reference 39

Resolution
verified exact
local_arxiv, observed 2026-08-07T13:15:15.961206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T13:15:12.161304Z digest=sha256:7b6c02c5ca5b0028aa5556c9243c4eec277a07ad615cfdceaa27a7ddbc20d1ba

Observation d01e6972-0e46-4606-bb0b-9b6968fe551e · inbound

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data cites this paper.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Non-Gaussian Process Regression

Reference 64

Resolution
unresolved
no resolver link, observed 2026-07-13T20:27:45.196743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T20:27:45.196743Z digest=sha256:6463078cc8ed51d56710a811017e54372ce920f4c7a2284200c40598253c01b2