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Paper Citation Record · LEDGER

A Primer on Bayesian Neural Networks: Review and Debates

As of 21 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2309.16314.

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

pith.paper-citation-record.v1
2309.16314 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 2026-07-21T06:31:05.380196+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T23:33:16.496226Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T22:29:00.690047Z

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 5454adf2-2216-462d-bb4a-1bfe4a24a805 · inbound

Minimaxity and Admissibility of Bayesian Neural Networks cites this paper.

Minimaxity and Admissibility of Bayesian Neural Networks A Primer on Bayesian Neural Networks: Review and Debates

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-10T22:45:50.634344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

source=pdf_text observed=2026-05-10T19:34:41.269341Z digest=sha256:74e0b693e11e7ce87253af438da1f405ad302bf69649b4fdd5f3e9e9a18ca10e

Observation 78d16ac1-761b-4e02-8ee4-ea8771022436 · inbound

Reconstructing Galactic Gravitational Potentials from Stellar Kinematics with Physics-Informed Neural Networks cites this paper.

Reconstructing Galactic Gravitational Potentials from Stellar Kinematics with Physics-Informed Neural Networks A Primer on Bayesian Neural Networks: Review and Debates

Reference 20

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T22:29:00.691562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

source=arxiv_source observed=2026-06-26T23:33:16.496226Z digest=sha256:aa4fff2d7ade93c5a5c007e54ec54f7ba72fec9a218ef320d0c1a8c4517ad996