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Paper Citation Record · LEDGER

A Scalable Variational Bayes Approach to Fit High-dimensional Spatial Generalized Linear Mixed Models

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2402.15705.

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

pith.paper-citation-record.v1
2402.15705 v3

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 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:56:04.001584Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T21:17:48.490896Z

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 31b70b96-8e07-4c73-8494-09d33d668a3c · inbound

Fast Variational Bayes for Large Spatial Data cites this paper.

Fast Variational Bayes for Large Spatial Data A Scalable Variational Bayes Approach to Fit High-dimensional Spatial Generalized Linear Mixed Models

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-06T16:56:04.001584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:56:04.001584Z digest=sha256:cc58ce9600fbd2cc2c77d82fa5010847208e7d7149435fe046f0c6df36f4b893

Observation a8d2b97b-eb73-434d-9de9-e37c501263cb · inbound

A Cubing Strategy for Identifying Stable Hyperparameter Regions for Uncertainty Quantification in Spatial Deep Learning cites this paper.

A Cubing Strategy for Identifying Stable Hyperparameter Regions for Uncertainty Quantification in Spatial Deep Learning A Scalable Variational Bayes Approach to Fit High-dimensional Spatial Generalized Linear Mixed Models

Reference 193

Resolution
verified exact
arxiv_id, observed 2026-05-19T21:17:48.493916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-19T21:14:45.344663Z digest=sha256:85af7cdd782991d6dc46708d34983d76b11ef44e62743f08ead32404e5165b11