Paper Citation Record · LEDGER

BayesFlow: Learning complex stochastic models with invertible neural networks

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2003.06281.

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

pith.paper-citation-record.v1
2003.06281 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:49:17.168293Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

10
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3208d265-f5a4-4ce1-8f4e-925b83504e5d · inbound

Extending Evidence Accumulation Models to Bounded Continuous Self-report Data cites this paper.

Extending Evidence Accumulation Models to Bounded Continuous Self-report Data BayesFlow: Learning complex stochastic models with invertible neural networks

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-09T02:39:51.248522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 97e4b895-936d-4409-a28e-de5099036859 · inbound

Extending Evidence Accumulation Models to Bounded Continuous Self-report Data cites this paper.

Extending Evidence Accumulation Models to Bounded Continuous Self-report Data BayesFlow: Learning complex stochastic models with invertible neural networks

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:21:18.540847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4ab0a577-93bb-43c2-834d-1da4d9b361ff · inbound

Amortized Energy-Based Bayesian Inference cites this paper.

Amortized Energy-Based Bayesian Inference BayesFlow: Learning complex stochastic models with invertible neural networks

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:58:58.821989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f15bd9ed-1dca-419b-a169-150d3e520745 · inbound

GenSBI: Generative Methods for Simulation-Based Inference in JAX cites this paper.

GenSBI: Generative Methods for Simulation-Based Inference in JAX BayesFlow: Learning complex stochastic models with invertible neural networks

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-06-29T18:53:51.757975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 02fa741f-1fe3-4e07-ac5a-c5d2890c2983 · inbound

One Generator, Any Process: LLM-Conditioning for the LHC cites this paper.

One Generator, Any Process: LLM-Conditioning for the LHC BayesFlow: Learning complex stochastic models with invertible neural networks

Reference 189

Resolution: verified exact; arxiv_id, observed 2026-06-26T07:59:09.003372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 43535dc2-4911-475d-835a-e8d1d96e902e · inbound

One Generator, Any Process: LLM-Conditioning for the LHC cites this paper.

One Generator, Any Process: LLM-Conditioning for the LHC BayesFlow: Learning complex stochastic models with invertible neural networks

Reference 193

Resolution: verified exact; arxiv_id, observed 2026-06-30T10:14:35.312615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 60551118-3520-4fd2-8e19-1d07da77baac · inbound

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems cites this paper.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems BayesFlow: Learning complex stochastic models with invertible neural networks

Reference 35

Resolution: metadata mismatch; local_arxiv, observed 2026-07-08T12:14:51.353677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0e047b9e-f157-4ba2-a2b4-a9f6190a159d · inbound

Divide-and-Conquer: Towards Generalizable Amortized Bayesian Inference for the Drift Diffusion Model cites this paper.

Divide-and-Conquer: Towards Generalizable Amortized Bayesian Inference for the Drift Diffusion Model BayesFlow: Learning complex stochastic models with invertible neural networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T16:49:17.168293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.