Paper Citation Record · LEDGER

Position: There Is No Free Bayesian Uncertainty Quantification

As of 17 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2506.03670.

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

pith.paper-citation-record.v1
2506.03670 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:04:05.762308Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

29 of 29 outbound references displayed

verified exact1
verified fuzzy23
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1a5bc2db-36cc-4dbd-9acf-aeee163e5595 · outbound

This paper cites Concentration of tempered posteriors and of their variational approximations.

Position: There Is No Free Bayesian Uncertainty Quantification Concentration of tempered posteriors and of their variational approximations

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:06.103234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4fa396c8-acc9-4a81-86be-9db521bb82eb · outbound

This paper cites Bayesian neural networks via mcmc: a python-based tutorial.

Position: There Is No Free Bayesian Uncertainty Quantification Bayesian neural networks via mcmc: a python-based tutorial

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:06.092193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f18f7ae6-a8e5-434b-b8ee-67dff703cc77 · outbound

This paper cites Bayesian graph convolutional neural networks via tempered mcmc.

Position: There Is No Free Bayesian Uncertainty Quantification Bayesian graph convolutional neural networks via tempered mcmc

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:06.080737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6579c116-c28a-45ce-a78e-dc395c33aaba · outbound

This paper cites Safe learning: bridging the gap between bayes, mdl and statistical learning theory via empirical convexity.

Position: There Is No Free Bayesian Uncertainty Quantification Safe learning: bridging the gap between bayes, mdl and statistical learning theory via empirical convexity

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:06.070162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 23f32a31-2792-4707-88d0-2a2df0621edf · outbound

This paper cites Minimum description length revisited.

Position: There Is No Free Bayesian Uncertainty Quantification Minimum description length revisited

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:06.059511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3b75b328-b345-4f2f-89b5-14e20a16fcc3 · outbound

This paper cites A tight excess risk bound via a unified pac-bayesian--rademacher--shtarkov--mdl complexity.

Position: There Is No Free Bayesian Uncertainty Quantification A tight excess risk bound via a unified pac-bayesian--rademacher--shtarkov--mdl complexity

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:06.048995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a6f51306-3555-4802-9bde-1eebbae4bd7f · outbound

This paper cites A Primer on PAC-Bayesian Learning.

Position: There Is No Free Bayesian Uncertainty Quantification A Primer on PAC-Bayesian Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:05.687347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be225f8c-3593-413b-84c9-a10cb7c3cea1 · outbound

This paper cites Bootstrap.

Position: There Is No Free Bayesian Uncertainty Quantification Bootstrap

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:06.038423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e5794f99-1104-4c4d-a424-c65b01e04446 · outbound

This paper cites Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods.

Position: There Is No Free Bayesian Uncertainty Quantification Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:05.694178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17eb1289-bbff-4f75-b032-f6d02f9a2dd8 · outbound

This paper cites An optimization-centric view on bayes' rule: Reviewing and generalizing variational inference.

Position: There Is No Free Bayesian Uncertainty Quantification An optimization-centric view on bayes' rule: Reviewing and generalizing variational inference

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:06.021117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fd63f597-d29f-4594-90f6-18e0dd4c2106 · outbound

This paper cites Being bayesian, even just a bit, fixes overconfidence in relu networks.

Position: There Is No Free Bayesian Uncertainty Quantification Being bayesian, even just a bit, fixes overconfidence in relu networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:06.010325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c95e2b20-227c-482f-adf4-a553b37a6285 · outbound

This paper cites Bayesian neural networks and density networks.

Position: There Is No Free Bayesian Uncertainty Quantification Bayesian neural networks and density networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:05.999453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1eb2902a-3bf0-417c-8865-74f640300136 · outbound

This paper cites Simplified pac-bayesian margin bounds.

Position: There Is No Free Bayesian Uncertainty Quantification Simplified pac-bayesian margin bounds

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:05.989127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c53fe083-dfeb-4927-b5a6-7b85289a385a · outbound

This paper cites Some pac-bayesian theorems.

Position: There Is No Free Bayesian Uncertainty Quantification Some pac-bayesian theorems

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:05.978104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 88ac59a1-4ac2-4c96-8d59-9970b46b3135 · outbound

This paper cites Pac-bayesian stochastic model selection.

Position: There Is No Free Bayesian Uncertainty Quantification Pac-bayesian stochastic model selection

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:05.966020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b188b5ed-adf0-4562-852b-12a6b337dfde · outbound

This paper cites Probabilistic machine learning: an introduction.

Position: There Is No Free Bayesian Uncertainty Quantification Probabilistic machine learning: an introduction

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:05.954191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c3d0ad7b-8535-4878-be1b-20ba8bf4ad34 · outbound

This paper cites Why are bootstrapped deep ensembles not better? In ''I Can't Believe It's Not Better!''NeurIPS 2020 workshop, 2020.

Position: There Is No Free Bayesian Uncertainty Quantification Why are bootstrapped deep ensembles not better? In ''I Can't Believe It's Not Better!''NeurIPS 2020 workshop, 2020

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:05.942116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 09bfcdc1-5fca-4cc9-a86b-c09a46cf2596 · outbound

This paper cites PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction.

Position: There Is No Free Bayesian Uncertainty Quantification PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:05.725202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e96c0962-55a2-4434-b262-5d5034dd1167 · outbound

This paper cites PAC Confidence Predictions for Deep Neural Network Classifiers.

Position: There Is No Free Bayesian Uncertainty Quantification PAC Confidence Predictions for Deep Neural Network Classifiers

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:04:05.810126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation df490f4e-53b8-47bc-93d6-82ed39c39f53 · outbound

This paper cites A comparison of the Bayesian and frequentist approaches to estimation, volume 24.

Position: There Is No Free Bayesian Uncertainty Quantification A comparison of the Bayesian and frequentist approaches to estimation, volume 24

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:05.930971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 43951b5d-2b3e-4b7d-9f8c-847550032231 · outbound

This paper cites Machine learning: a Bayesian and optimization perspective.

Position: There Is No Free Bayesian Uncertainty Quantification Machine learning: a Bayesian and optimization perspective

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:05.920041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8cac6ca5-c87a-44a4-a256-d5cb37ef42a1 · outbound

This paper cites Bayesian inference: An introduction to principles and practice in machine learning.

Position: There Is No Free Bayesian Uncertainty Quantification Bayesian inference: An introduction to principles and practice in machine learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:05.908970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 82209515-a98b-4641-9b8a-77bc947dbf06 · outbound

This paper cites Asymptotic statistics, volume 3.

Position: There Is No Free Bayesian Uncertainty Quantification Asymptotic statistics, volume 3

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:05.742791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4abdf21e-f56d-435b-b658-31fccfce7e7d · outbound

This paper cites On mcmc sampling in bayesian mlp neural networks.

Position: There Is No Free Bayesian Uncertainty Quantification On mcmc sampling in bayesian mlp neural networks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:05.889870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 797a4b26-5fec-490e-910f-337606fab5c8 · outbound

This paper cites Frequentist inference.

Position: There Is No Free Bayesian Uncertainty Quantification Frequentist inference

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:05.878298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3b4c75a4-208a-4163-b4c4-d3831632ac13 · outbound

This paper cites All of statistics: a concise course in statistical inference.

Position: There Is No Free Bayesian Uncertainty Quantification All of statistics: a concise course in statistical inference

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:05.866793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d8b63e82-0e18-4581-87df-1a0071b6e305 · outbound

This paper cites How Good is the Bayes Posterior in Deep Neural Networks Really?.

Position: There Is No Free Bayesian Uncertainty Quantification How Good is the Bayes Posterior in Deep Neural Networks Really?

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:05.755461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29b86731-4b61-4ce3-aeb5-b6b01819512c · outbound

This paper cites u nnemann, and David R \.

Position: There Is No Free Bayesian Uncertainty Quantification u nnemann, and David R \

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:05.855249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e7f4e780-fe6c-4a3f-91cc-6d7419047b18 · outbound

This paper cites Optimal information processing and bayes's theorem.

Position: There Is No Free Bayesian Uncertainty Quantification Optimal information processing and bayes's theorem

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:05.844702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.