Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2110.15335.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T19:39:40.115786Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T12:49:52.753358Z
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 98ae7a5f-f14f-483a-a690-b9fe93fd5f44 · inbound
Variational Sequential Optimal Experimental Design using Reinforcement Learning cites this paper.
Variational Sequential Optimal Experimental Design using Reinforcement Learning Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T08:24:11.187412Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 9d7dab90-d802-4981-b8b5-3dfbfa3b0c82 · inbound
Optimal experimental design: Formulations and computations cites this paper.
Optimal experimental design: Formulations and computations Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T23:13:36.341374Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 870bc0b3-26c2-4679-81a6-97958f1d1376 · inbound
Active Learning of Model Discrepancy with Bayesian Experimental Design cites this paper.
Active Learning of Model Discrepancy with Bayesian Experimental Design Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:39:40.115786Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 74fca89e-4991-4ff1-b45d-d5cc4ccb8311 · inbound
Adaptive Sensing beyond Non-Adaptive Information Limits: End-to-End Co-Design of Geometry, Policy, and Inference cites this paper.
Adaptive Sensing beyond Non-Adaptive Information Limits: End-to-End Co-Design of Geometry, Policy, and Inference Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning
Reference 34
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-12T00:01:16.832365Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation f35fc61c-24e1-4fb2-9dbf-1c7a1dbc1117 · inbound
Action-BED: Task-Driven Bayesian Experimental Design with Singly Intractable Objectives cites this paper.
Action-BED: Task-Driven Bayesian Experimental Design with Singly Intractable Objectives Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning
Reference 63
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T12:49:52.754567Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.