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Paper Citation Record · LEDGER

Bayesian error propagation for neural-net based parameter inference

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2205.11587.

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

pith.paper-citation-record.v1
2205.11587 v2

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 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:43:16.437405Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

0 of 0 outbound references displayed

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  • 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 ebd8c008-07f2-481b-ad74-711b7883b5aa · inbound

Cosmo-Learn: code for learning cosmology using different methods and mock data cites this paper.

Cosmo-Learn: code for learning cosmology using different methods and mock data Bayesian error propagation for neural-net based parameter inference

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-05T14:43:16.437405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:43:16.437405Z digest=sha256:e364e690e33307a5bf0a9419a54c76a240e579b5a966c0bc1350c622d23398b6

Observation fe1b6196-d2ca-4273-ae78-799e8fe17b89 · inbound

Using Neural Emulators and Hamiltonian Monte Carlo to constrain the Epoch of Reionization's History with the Ly$\alpha$ Forest Power Spectrum cites this paper.

Using Neural Emulators and Hamiltonian Monte Carlo to constrain the Epoch of Reionization's History with the Ly$\alpha$ Forest Power Spectrum Bayesian error propagation for neural-net based parameter inference

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-04T16:34:37.730018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T16:34:37.730018Z digest=sha256:108c4b91b56a32de3c332f4eef6cb252ba96223ae5e74a231e24977005ad63ed