Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Differentiable Bootstrap Particle Filters for Regime-Switching Models
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2302.10319.
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 2026-08-12T14:11:16.504139Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-12T14:09:56.287600Z
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 ede71caf-51d8-481f-a063-80848a76a1c9 · inbound
GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models cites this paper.
GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Differentiable Bootstrap Particle Filters for Regime-Switching Models
Reference 25
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T14:09:56.295156Z
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 0ac5fe17-63df-4ddf-b5f8-aed508faf553 · inbound
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks cites this paper.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Differentiable Bootstrap Particle Filters for Regime-Switching Models
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T14:11:16.504139Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.