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Paper Citation Record · LEDGER

Likelihood-free MCMC with Amortized Approximate Ratio Estimators

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 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 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:45:59.436912Z

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

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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 ceb4e639-a103-46d6-8082-a9356c8feac5 · inbound

Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning cites this paper.

Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Reference 73

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unresolved
no resolver link, observed 2026-08-14T05:10:13.701861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T05:10:13.701861Z digest=sha256:4441f9853be52fa7f167dbff90d6cee199422af2422bac456d731cd775687e31

Observation 6ac7753d-33b4-41df-8020-a188cde1e263 · inbound

sbi reloaded: a toolkit for simulation-based inference workflows cites this paper.

sbi reloaded: a toolkit for simulation-based inference workflows Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Reference 3

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no resolver link, observed 2026-08-12T12:16:12.577187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T12:16:12.577187Z digest=sha256:f59862875991c9b9f73c2378bdc4d62545d8e12da4b8398f6eaabfeab09eacf6

Observation 2b61a1f1-a1fd-4025-8f58-7752835b3668 · inbound

Radio pulsar population synthesis with consistent flux measurements using simulation-based inference cites this paper.

Radio pulsar population synthesis with consistent flux measurements using simulation-based inference Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Reference 18

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unresolved
no resolver link, observed 2026-08-11T21:53:36.980359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T21:53:36.980359Z digest=sha256:c64c757a619559f582b4368cbcb8dad201604f6fc2e7262c541469df3c24fbbe

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

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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:fb78e30804023c4caf4b3bd514615be9c39792276206e6773cf1606c003cbcf9

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=sha256:298d57c1d39f11ee4004aac6d1ee8d5af35227cc28fb0190bacffc3e399e8a91

Observation 47e59cfb-84c3-4ca9-8eeb-60eeadbc68f5 · inbound

A robust neural determination of the source-count distribution of the Fermi-LAT sky at high latitudes cites this paper.

A robust neural determination of the source-count distribution of the Fermi-LAT sky at high latitudes Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-16T00:45:59.436912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:45:59.436912Z digest=sha256:c7cfaf46d8f3b7ebde9b2f1cb9590730d7a6f74ea46abc57d046e2a3e5738637

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=sha256:42098826b353e7eff0366c63db85102884288e55068e4a2d4300c3feb5d1fcbf

Observation 49ced8b6-40c4-4807-95e6-f81d02132900 · inbound

Simulation-Based Inference: A Practical Guide cites this paper.

Simulation-Based Inference: A Practical Guide Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-15T17:21:41.076790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:21:41.076790Z digest=sha256:05ed1c71637b9c375c7daa64c047c30fa37cf7ecfd9f91b54e57dfcbd94b0be4

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T18:44:35.962392Z digest=sha256:dc889068f54e6cce8f18e4f71f5e5570c8422479b0ea0ab9e876fac0e0030b91

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

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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 2026-08-21T06:32:19.484+00:00.

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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 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-06-27T15:22:40.822607Z digest=sha256:88add46c75ec95d18261443f9e5bcb7f5f44db02f9121807c99a6266f82c4697

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 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-06-27T15:19:55.822415Z digest=sha256:005ca98335e38d0423694cf546e5195c72ac8fdb6984f14df20819d41165f35a

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.

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