Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
High dimensional Bayesian inference for Gaussian directed acyclic graph models
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1109.4371.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-14T13:24:41.449209Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-10T07:36:57.976358Z
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 17b57d03-8763-4455-a02a-a8f3c18321b0 · inbound
Bayesian DAG Structure Learning with Simultaneous Shrinkage Covariance Estimation under Scale-Mixture Error Distributions in the Proportional High-Dimensional Regime cites this paper.
Bayesian DAG Structure Learning with Simultaneous Shrinkage Covariance Estimation under Scale-Mixture Error Distributions in the Proportional High-Dimensional Regime High dimensional Bayesian inference for Gaussian directed acyclic graph models
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-07-10T07:36:57.977754Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 44e2a1f2-6bac-4312-9d0c-5852e2b54f01 · inbound
Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks cites this paper.
Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks High dimensional Bayesian inference for Gaussian directed acyclic graph models
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T13:24:41.449209Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.