Paper Citation Record · LEDGER

Likelihood-free MCMC with Amortized Approximate Ratio Estimators

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 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 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:15:01.542661Z

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 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.

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 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.

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.