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Paper Citation Record · LEDGER

Likelihood-free MCMC with Amortized Approximate Ratio Estimators

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:1903.04057.

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

pith.paper-citation-record.v1
1903.04057 v5

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 2026-08-10T06:31:04.303077+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T13:21:25.343303Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, 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

27
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 29388067-d912-44bf-a12c-001e9becbbf6 · inbound

Normalizing Flow-Assisted Nested Sampling on Type-II Seesaw Model cites this paper.

Normalizing Flow-Assisted Nested Sampling on Type-II Seesaw Model Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-10T13:21:25.343303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T13:21:25.343303Z digest=sha256:fb156c651e0acd1ba4811caaf320b5722fd63b09d65946533d304c16f6726990

Observation e43ffbdc-ba74-42bb-907b-be88e7045a52 · inbound

Towards characterizing dark matter subhalo perturbations in stellar streams with graph neural networks cites this paper.

Towards characterizing dark matter subhalo perturbations in stellar streams with graph neural networks Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-09T04:39:46.614742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T04:39:46.614742Z digest=sha256:fc03a398a30de66cf5f1112b1209ce7e51854bf617dcbc02cb6aa1a089e0a940

Observation aaec77f8-6899-4320-bdea-1b43d224fb9c · inbound

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning cites this paper.

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-07T11:15:01.542661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:15:01.542661Z digest=sha256:3a8d49fe427b1b166eafab2cd8412d1c22dc6a7da7e1068ff37d4ae1f3bd801c

Observation 27b58de2-c4eb-4dac-9574-7d5004a406ba · inbound

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

GenSBI: Generative Methods for Simulation-Based Inference in JAX Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-06-29T18:53:51.769621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-29T18:44:35.962392Z digest=sha256:2bc5f4c225249f7c4b0b33bf3c2d0647df494585281b85e61e8657211e2f0f84

Observation bc16c9be-ab16-409e-bd52-1fc9c42c44e0 · inbound

21cmEMUv3: a hybrid diffusion-LSTM emulator of 21cmFAST summary observables cites this paper.

21cmEMUv3: a hybrid diffusion-LSTM emulator of 21cmFAST summary observables Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Reference 179

Resolution
metadata mismatch
arxiv_id, observed 2026-06-28T22:22:43.194158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-28T20:53:04.280649Z digest=sha256:c001e0bc8803885ef27a6c756f85068c458a5959a2cc1748dab7a028732ec51e

Observation eeee1b29-214d-47ae-9fa6-696ebcabe84c · inbound

Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions cites this paper.

Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Reference 45

Resolution
verified exact
arxiv_id, observed 2026-06-27T19:11:10.582184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-27T15:22:40.822607Z digest=sha256:ceac285affd7aa860d04fa0fb28eaafb345ae493c47f901d35956f7e0f7f034f

Observation 184dee02-03b3-4369-b1b8-c413a9cb3655 · inbound

Learning the Universe with cosmological rescaling of merger trees and semi-analytic galaxy formation models cites this paper.

Learning the Universe with cosmological rescaling of merger trees and semi-analytic galaxy formation models Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Reference 35

Resolution
metadata mismatch
arxiv_id, observed 2026-06-27T15:20:59.702630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-27T15:19:55.822415Z digest=sha256:d4e1c42478ea16940d46cafc4725c31124cd558bf78f58cd9aa14d23f8268ae8

Observation e87448a6-10d2-4e0f-94c5-20bf2b5b1832 · inbound

A Simulation Based Inference Approach to Modelling of Type Ia Supernova Populations cites this paper.

A Simulation Based Inference Approach to Modelling of Type Ia Supernova Populations Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-03T00:38:50.882371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T00:38:50.882371Z digest=sha256:6bb761703ce652bc2797bafeaf9de4558aaaf67e2d90e980d0a075f420904055