Paper Citation Record · LEDGER

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows

As of 11 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2512.04954.

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

pith.paper-citation-record.v1
2512.04954 v3

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T06:42:57.466356Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

27 of 27 outbound references displayed

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unresolved27
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External citation measurements

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Outbound references

Observation f49790b2-e1f0-41e2-83d4-659b8f26db86 · outbound

This paper cites Tarantola,Inverse problem theory and methods for model parameter estimation, SIAM (2005).

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Tarantola,Inverse problem theory and methods for model parameter estimation, SIAM (2005)

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:55.527897Z

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Observation 9dc4f2cc-d905-4ceb-8ca9-d2351849be59 · outbound

This paper cites Cranmer, J.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Cranmer, J

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:55.590915Z

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Observation 568e5454-791b-46e4-8047-bd6bd335cf0e · outbound

This paper cites Metropolis, A.W.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Metropolis, A.W

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:55.647436Z

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Observation d2bb3c8b-9a9d-4108-8029-111b65afea1b · outbound

This paper cites Hastings,Monte carlo sampling methods using markov chains and their applications, Biometrika57(1970) 97.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Hastings,Monte carlo sampling methods using markov chains and their applications, Biometrika57(1970) 97

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:55.707033Z

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Observation 9469488b-a2e1-433b-b775-371318577593 · outbound

This paper cites Skilling,Nested sampling for general bayesian computation,Bayesian analysis1(2006) 833.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Skilling,Nested sampling for general bayesian computation,Bayesian analysis1(2006) 833

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:55.773426Z

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Observation f2f39252-819d-417b-9e14-f81547855d5f · outbound

This paper cites Foreman-Mackey, D.W.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Foreman-Mackey, D.W

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:55.844270Z

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Observation 97d2b0ce-f8b8-48ab-a1af-2941686d027d · outbound

This paper cites Speagle,dynesty: a dynamic nested sampling package for estimating bayesian posteriors and evidences,Monthly Notices of the Royal Astronomical Society493(2020) 3132.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Speagle,dynesty: a dynamic nested sampling package for estimating bayesian posteriors and evidences,Monthly Notices of the Royal Astronomical Society493(2020) 3132

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:55.908162Z

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Observation 91d7bcab-4274-4243-ac5a-57617ef3abf5 · outbound

This paper cites Brehmer, K.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Brehmer, K

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:55.968388Z

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Observation f86da42d-81cc-4865-82ff-067aecf2b974 · outbound

This paper cites Tabak and E.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Tabak and E

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:56.039271Z

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Observation e6a69dfb-a5c4-4a4a-9c2b-554f001afdb9 · outbound

This paper cites Rezende and S.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Rezende and S

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:56.082536Z

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Observation 8ea78f84-1c27-4c03-9d02-c62813aa488a · outbound

This paper cites Papamakarios, E.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Papamakarios, E

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:56.153348Z

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Observation c58c61ad-a306-4631-b9b5-f10d7b5b378a · outbound

This paper cites Density estimation using Real NVP.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Density estimation using Real NVP

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:56.218557Z

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Observation a030839d-8c06-4678-9d12-dae80d78a477 · outbound

This paper cites Durkan, A.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Durkan, A

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:56.285717Z

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Observation 8891cbe7-8a53-4ca1-81a3-0c287aa55fb6 · outbound

This paper cites Papamakarios, T.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Papamakarios, T

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:56.344303Z

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Observation 2fcdde1f-231a-414d-a0bc-a3b2ddc41762 · outbound

This paper cites Lueckmann, J.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Lueckmann, J

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:56.394414Z

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Observation 23f48b8c-3752-4a56-b823-9fc78066aa60 · outbound

This paper cites Papamakarios and I.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Papamakarios and I

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:56.576267Z

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Observation 81e95bc1-2889-493f-a5c9-57c76397c3e6 · outbound

This paper cites Greenberg, M.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Greenberg, M

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:56.669465Z

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Observation f00b4282-9c63-4496-b942-c48295b57158 · outbound

This paper cites an unresolved cited work.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Unresolved cited work

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:56.814792Z

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Observation beaee202-ac8c-4725-92f5-3a3e57c64746 · outbound

This paper cites Wirnsberger, A.J.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Wirnsberger, A.J

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:56.890118Z

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Observation f6ed96dd-e97d-4693-a058-2a6aa9561434 · outbound

This paper cites Optimal Investment, Heterogeneous Consumption and Best Time for Retirement.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Optimal Investment, Heterogeneous Consumption and Best Time for Retirement

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:56.949680Z

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Observation c89b2091-c45a-40b4-a767-46b2b18f4ea6 · outbound

This paper cites Probing intractable beyond-standard-model parameter spaces armed with Machine Learning.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Probing intractable beyond-standard-model parameter spaces armed with Machine Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:57.024694Z

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Observation eaed056d-8b63-4aa6-bade-feb36cfe5f3c · outbound

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

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Normalizing Flow-Assisted Nested Sampling on Type-II Seesaw Model

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:57.107400Z

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Observation d55bf6f5-0a7c-4a53-b352-e5a53352e3d9 · outbound

This paper cites M¨ uller, B.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows M¨ uller, B

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:57.191129Z

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Observation 1abab616-d782-4db4-8d84-73af7e3d7310 · outbound

This paper cites Kullback and R.A.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Kullback and R.A

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:57.267810Z

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Observation 85754383-64c1-4ebc-afff-7bba2d7645bd · outbound

This paper cites Stimper, D.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Stimper, D

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:57.323606Z

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Observation 356195f3-ddf3-4595-b22c-abb958569a23 · outbound

This paper cites Ngairangbam, B.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows Ngairangbam, B

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:57.407740Z

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Observation 36664d43-b8b8-49f6-befa-1c6156c5ce2e · outbound

This paper cites On the Need for Topology-Aware Generative Models for Manifold-Based Defenses.

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows On the Need for Topology-Aware Generative Models for Manifold-Based Defenses

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T06:42:57.466356Z

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Pith citing papers

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