Paper Citation Record · LEDGER

Quantification of Credal Uncertainty: A Distance-Based Approach

As of 8 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2603.27270.

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

pith.paper-citation-record.v1
2603.27270 v2

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-15T11:46:17.889059Z

measured 20 of 20 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T20:50:20.633948Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:59:19.963536Z

Reference resolution

19 of 19 outbound references displayed

verified exact0
verified fuzzy0
unresolved18
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9a4ebd47-e37f-40bc-af8e-22b16ac86574 · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

Quantification of Credal Uncertainty: A Distance-Based Approach Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea8c30eb-78a4-4909-b1db-f4db5373b023 · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

Quantification of Credal Uncertainty: A Distance-Based Approach Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ee265d5-2962-48f5-901b-2d246bc47b73 · outbound

This paper cites an unresolved cited work.

Quantification of Credal Uncertainty: A Distance-Based Approach Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8694856b-82a9-4fac-a1ce-70dcd1d0e91f · outbound

This paper cites Kaplan, and Melih Kandemir.

Quantification of Credal Uncertainty: A Distance-Based Approach Kaplan, and Melih Kandemir

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec0cc4e6-a3c4-4839-9d75-f782a41c59a9 · outbound

This paper cites Credal bayesian deep learning.Trans.

Quantification of Credal Uncertainty: A Distance-Based Approach Credal bayesian deep learning.Trans

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f9647da-fb9e-4273-a901-1a158af257aa · outbound

This paper cites Timo Löhr, Paul Hofman, Felix Mohr, and Eyke Hüllermeier.

Quantification of Credal Uncertainty: A Distance-Based Approach Timo Löhr, Paul Hofman, Felix Mohr, and Eyke Hüllermeier

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c003de9-3473-42e4-9564-bf4737262a52 · outbound

This paper cites Second-Order Uncertainty Quantification: A Distance-Based Approach.

Quantification of Credal Uncertainty: A Distance-Based Approach Second-Order Uncertainty Quantification: A Distance-Based Approach

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 107b6f6a-dbd1-44bd-a1c8-557efb78c18e · outbound

This paper cites A Rate-Distortion View of Uncertainty Quantification.

Quantification of Credal Uncertainty: A Distance-Based Approach A Rate-Distortion View of Uncertainty Quantification

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da04f944-ec4a-4f75-a0da-fa56288d6e18 · outbound

This paper cites Quantifying epistemic predictive uncertainty in conformal prediction.arXiv preprint arXiv:2602.01667,.

Quantification of Credal Uncertainty: A Distance-Based Approach Quantifying epistemic predictive uncertainty in conformal prediction.arXiv preprint arXiv:2602.01667,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fec17322-a5c7-4196-86fa-e2c6d43a3295 · outbound

This paper cites Credal Bayesian Deep Learning.

Quantification of Credal Uncertainty: A Distance-Based Approach Credal Bayesian Deep Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59d9919d-b157-43f0-81e6-6ed94ece8385 · outbound

This paper cites Conformal Prediction Regions are Imprecise Highest Density Regions.

Quantification of Credal Uncertainty: A Distance-Based Approach Conformal Prediction Regions are Imprecise Highest Density Regions

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac10e588-476a-4fee-bdee-a4e796e1d1a6 · outbound

This paper cites A Category-Theoretic Analysis of Conformal Prediction.

Quantification of Credal Uncertainty: A Distance-Based Approach A Category-Theoretic Analysis of Conformal Prediction

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b6253af-a703-4654-9a53-71220b8435a8 · outbound

This paper cites Quantifying aleatoric and epistemic uncertainty: A credal approach.

Quantification of Credal Uncertainty: A Distance-Based Approach Quantifying aleatoric and epistemic uncertainty: A credal approach

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80aa58eb-095f-403e-9646-d45509064683 · outbound

This paper cites Axioms for uncertainty measures on belief functions and credal sets.

Quantification of Credal Uncertainty: A Distance-Based Approach Axioms for uncertainty measures on belief functions and credal sets

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9a1a216-2cbf-45fa-ae91-9e87787bf6c7 · outbound

This paper cites coherence.

Quantification of Credal Uncertainty: A Distance-Based Approach coherence

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bdb6c3cd-5148-4da6-9203-30fc67fa93ee · outbound

This paper cites These newer contributions build on a rich foundation in imprecise probability theory and uncertainty quantification Pal et al.

Quantification of Credal Uncertainty: A Distance-Based Approach These newer contributions build on a rich foundation in imprecise probability theory and uncertainty quantification Pal et al

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be03fe3f-d3fc-467b-92f5-c898c98eaf6f · outbound

This paper cites infimum) of a convex (resp.

Quantification of Credal Uncertainty: A Distance-Based Approach infimum) of a convex (resp

Reference 17

Resolution: malformed identifier; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26824b7d-00b8-45ad-a2bb-09713db8d375 · outbound

This paper cites [2025], a recent methodology that offers principled control over the composition and size of the credal set.

Quantification of Credal Uncertainty: A Distance-Based Approach [2025], a recent methodology that offers principled control over the composition and size of the credal set

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15dd29aa-1925-4863-80e8-c03de0815dcd · outbound

This paper cites Given a hypothesishand a dataset{(x i, yi)}N i=1, the likelihood is defined as L(h) = NY i=1 p(yi |x i, h).(8) Following Löhr et al.

Quantification of Credal Uncertainty: A Distance-Based Approach Given a hypothesishand a dataset{(x i, yi)}N i=1, the likelihood is defined as L(h) = NY i=1 p(yi |x i, h).(8) Following Löhr et al

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-15T11:46:17.889059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 4501d07c-0f6e-4678-8b6b-496a1c8afd3d · inbound

On the QUEST for Uncertainty Quantification via Highest Density Regions cites this paper.

On the QUEST for Uncertainty Quantification via Highest Density Regions Quantification of Credal Uncertainty: A Distance-Based Approach

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-15T02:22:02.053620Z

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.