Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T23:12:41.384448Z
Paper Citation Record · LEDGER
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression
As of 11 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2412.20884.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
55 of 55 outbound references displayed
- verified exact1
- verified fuzzy41
- unresolved12
- parse uncertain0
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation b721c68b-146f-429d-8ef8-65038fbda192 · outbound
This paper cites Hogg, and Michael O’Neil.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Hogg, and Michael O’Neil
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:42.080514Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 8efd9ab9-782d-4714-a67d-0a77651f15b0 · outbound
This paper cites Anderson.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Anderson
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:42.067110Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 0d06b03b-687e-4f3e-b164-c7be9d370116 · outbound
This paper cites Uniform approximation of common Gaussian process kernels using equispaced Fourier grids.Applied and Computational Harmonic Analysis, 71:101640, July 2024.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Uniform approximation of common Gaussian process kernels using equispaced Fourier grids.Applied and Computational Harmonic Analysis, 71:101640, July 2024
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:42.053279Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 5596b648-9da0-428e-b05d-1edb6a6498aa · outbound
This paper cites A Conceptual Introduction to Hamiltonian Monte Carlo.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression A Conceptual Introduction to Hamiltonian Monte Carlo
Reference 4
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9d3bb4cf-78c5-4c12-b660-dbd0bd466eb0 · outbound
This paper cites Boerner, Stephen Deems, Thomas R.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Boerner, Stephen Deems, Thomas R
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:42.039485Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation cead6ddf-5670-4be4-bbd1-2b2a21aa036f · outbound
This paper cites A randomized algorithm for approximating the log determinant of a symmetric positive definite matrix.Linear Algebra and its Applications, 533:95–117, November 2017.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression A randomized algorithm for approximating the log determinant of a symmetric positive definite matrix.Linear Algebra and its Applications, 533:95–117, November 2017
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:42.014829Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation d7f8d790-1198-45b2-8f9f-1fafaa6338f8 · outbound
This paper cites Kernel operations on the GPU, with Autodiff, without memory overflows.Journal of Machine Learning Research, 22(74):1–6, 2021.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Kernel operations on the GPU, with Autodiff, without memory overflows.Journal of Machine Learning Research, 22(74):1–6, 2021
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:42.001251Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 0f6dc711-1c6e-4a4b-9229-3b18b416743e · outbound
This paper cites Non-stationary anderson acceleration with optimized damping.Journal of Computational and Applied Mathematics, 451:116077, 2024.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Non-stationary anderson acceleration with optimized damping.Journal of Computational and Applied Mathematics, 451:116077, 2024
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.987361Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation ab226e61-62a0-4a9f-a667-c6bce6daf9f1 · outbound
This paper cites Stochastic gradient Hamiltonian Monte Carlo.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Stochastic gradient Hamiltonian Monte Carlo
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.975157Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 2c89061a-1b34-4353-854f-fa50452287bd · outbound
This paper cites Epperly, Joel A.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Epperly, Joel A
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.962383Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation f8be94c3-b54d-48f9-bb0b-58eb4322ff7d · outbound
This paper cites Forthcoming, November 2024.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Forthcoming, November 2024
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.950568Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 6ec552a8-f67e-4c55-945d-0e4f7c146069 · outbound
This paper cites an unresolved cited work.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Unresolved cited work
Reference 12
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation b12717cc-bd2d-4e2f-a771-8a8a598ba16a · outbound
This paper cites On randomized trace estimates for indefinite matrices with an application to determinants.Foundations of Computational Mathematics, 22(3):875– 903, June 2022.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression On randomized trace estimates for indefinite matrices with an application to determinants.Foundations of Computational Mathematics, 22(3):875– 903, June 2022
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.926915Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation a3b471df-b1f6-4bef-a7c6-a36ad814f0c2 · outbound
This paper cites an unresolved cited work.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Unresolved cited work
Reference 14
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T23:12:41.915004Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 28cdf64a-e14b-46d0-b16c-b92241740a0f · outbound
This paper cites Mission CO2ntrol: A statistical scientist’s role in remote sensing of atmospheric carbon dioxide.Journal of the American Statistical Association, 113(521):152–168, January 2018.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Mission CO2ntrol: A statistical scientist’s role in remote sensing of atmospheric carbon dioxide.Journal of the American Statistical Association, 113(521):152–168, January 2018
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.903042Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation e7efaaa3-5573-4b5a-9317-486853831046 · outbound
This paper cites Scalable log determinants for Gaussian process kernel learning.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Scalable log determinants for Gaussian process kernel learning
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.891457Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 5a70a28e-85fd-462a-9248-d8e99eb1daf9 · outbound
This paper cites an unresolved cited work.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Unresolved cited work
Reference 17
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T23:12:41.880536Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 40903c3e-c4ff-411f-af8f-26fff5a185dd · outbound
This paper cites A determinant-free method to simulate the parameters of large Gaussian fields.Stat, 6(1):271–281, 2017.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression A determinant-free method to simulate the parameters of large Gaussian fields.Stat, 6(1):271–281, 2017
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.869041Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation ea584fee-71ad-48e7-b295-ecd4f707d34f · outbound
This paper cites Rebholz, and Mengying Xiao.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Rebholz, and Mengying Xiao
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.857625Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 20b6bc87-3b32-4730-8d4e-723a19be0954 · outbound
This paper cites Paul Laiu, and Thomas Strohmer.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Paul Laiu, and Thomas Strohmer
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.846049Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation eca17950-31db-4db3-af7c-32ef5f97874d · outbound
This paper cites Convergence Analysis of the Alternating Anderson-Picard Method for Nonlinear Fixed-point Problems.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Convergence Analysis of the Alternating Anderson-Picard Method for Nonlinear Fixed-point Problems
Reference 21
- Resolution
- metadata mismatch
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation c9cdee60-2b9d-43e9-9340-74f5da5f09d7 · outbound
This paper cites emcee: The MCMC Hammer.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression emcee: The MCMC Hammer
Reference 22
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c82e61c9-a220-4254-aaa7-1b69d18ef6a8 · outbound
This paper cites Column and row subset selection using nuclear scores: algorithms and theory for Nyström approximation, CUR decomposition, and graph Laplacian reduction, 2024.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Column and row subset selection using nuclear scores: algorithms and theory for Nyström approximation, CUR decomposition, and graph Laplacian reduction, 2024
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.834486Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 04135670-3fc7-4db9-b1a0-992b76bbd0d4 · outbound
This paper cites Tropp, and Madeleine Udell.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Tropp, and Madeleine Udell
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.822392Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation cd605388-ab26-4d31-b537-2b7b6fbe49fb · outbound
This paper cites Fucito, E.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Fucito, E
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.809049Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation f83773e8-eced-4c70-8c5e-6d3c557ad6b4 · outbound
This paper cites Lang.Quantum Chromodynamics on the Lattice: An Introductory Presentation, volume 788 ofLecture Notes in Physics.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Lang.Quantum Chromodynamics on the Lattice: An Introductory Presentation, volume 788 ofLecture Notes in Physics
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.798005Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation c368efaa-b0e6-440e-90e0-5ab9481a9c1b · outbound
This paper cites Riemann manifold Langevin and Hamiltonian Monte Carlo methods.Journal of the Royal Statistical Society Series B: Statistical Methodology, 73(2):123– 214, March 2011.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Riemann manifold Langevin and Hamiltonian Monte Carlo methods.Journal of the Royal Statistical Society Series B: Statistical Methodology, 73(2):123– 214, March 2011
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.786927Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 34a4d8b3-c16d-4eb8-b6f9-0ebde2d6ae78 · outbound
This paper cites Ensemble samplers with affine invariance.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Ensemble samplers with affine invariance
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.774696Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 978acb7b-a390-4ea6-bf37-d9b2a34fc8ff · outbound
This paper cites Equispaced Fourier representations for efficient Gaussian process regression from a billion data points.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Equispaced Fourier representations for efficient Gaussian process regression from a billion data points
Reference 29
- Resolution
- verified exact
- local_arxiv, observed 2026-08-10T23:12:41.424163Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 8b592424-e063-4cac-b927-c7468f06cde0 · outbound
This paper cites Stuart, and Sebastian J.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Stuart, and Sebastian J
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.764112Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 0ad91960-2a8a-4312-bda3-e324522169ad · outbound
This paper cites Higham, and Lloyd N.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Higham, and Lloyd N
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.753457Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 8d1f576b-3002-445e-aace-8b2fe54f529c · outbound
This paper cites Hancock, Jeremy Fischer, John Michael Lowe, Winona Snapp-Childs, Marlon Pierce, SureshMarru, J.EricCoulter, MatthewVaughn, BrianBeck, NiravMerchant, EdwinSkidmore, and Gwen Jacobs.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Hancock, Jeremy Fischer, John Michael Lowe, Winona Snapp-Childs, Marlon Pierce, SureshMarru, J.EricCoulter, MatthewVaughn, BrianBeck, NiravMerchant, EdwinSkidmore, and Gwen Jacobs
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.743244Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 58c1469e-2734-4381-8c5c-285f301dbac0 · outbound
This paper cites Inference in deep Gaussian processes using stochastic gradient Hamiltonian Monte Carlo.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Inference in deep Gaussian processes using stochastic gradient Hamiltonian Monte Carlo
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.732050Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 1e349a1d-9f2b-4e15-881b-1c79405f01ff · outbound
This paper cites Non-stationary Gaussian process regression with Hamiltonian Monte Carlo.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Non-stationary Gaussian process regression with Hamiltonian Monte Carlo
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.720205Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation ecd3931f-a2e6-484a-b292-41462bc52829 · outbound
This paper cites A unified view of some numerical methods for fractional diffusion.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression A unified view of some numerical methods for fractional diffusion
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.708697Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation b93b6f07-68e4-4d1a-9724-0be6c1083f59 · outbound
This paper cites Codeforagradient-basedanddeterminant-freeframeworkforfullybayesian gaussian process regression, November 2024.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Codeforagradient-basedanddeterminant-freeframeworkforfullybayesian gaussian process regression, November 2024
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.697715Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 60aaca97-7650-47f2-b2c0-26bd31faba70 · outbound
This paper cites Michael Kielstra and Michael Lindsey.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Michael Kielstra and Michael Lindsey
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.686119Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation e26f42fc-5f3e-46d7-83e8-e4bd17d7e59c · outbound
This paper cites Knoll and D.E.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Knoll and D.E
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.674291Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 6db23842-39e5-405c-9751-d61ff8c16e41 · outbound
This paper cites Approximate inference for fully Bayesian Gaussian process regression.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Approximate inference for fully Bayesian Gaussian process regression
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.661631Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation f842ad7d-92ed-4841-9be1-758c600f0f2b · outbound
This paper cites Teckentrup, and Simon Urbainczyk.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Teckentrup, and Simon Urbainczyk
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.649934Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation cbbb584f-f51e-42ac-8945-9cac20f0d6f5 · outbound
This paper cites Albergo, and Michael Lindsey.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Albergo, and Michael Lindsey
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.638924Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation c2d12254-c268-4870-9670-12fd826f24e6 · outbound
This paper cites Neal.MCMC Using Hamiltonian Dynamics.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Neal.MCMC Using Hamiltonian Dynamics
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.625893Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation ad9822ad-4ceb-4015-aa61-d276bd6494ef · outbound
This paper cites Noack, Harinarayan Krishnan, Mark D.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Noack, Harinarayan Krishnan, Mark D
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.613520Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 706abe73-4817-43e2-81a6-2d45ee966dec · outbound
This paper cites Noack, Hengrui Luo, and Mark D.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Noack, Hengrui Luo, and Mark D
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.601856Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 9b8ddf19-98f0-4eb1-908c-128b00f1274d · outbound
This paper cites an unresolved cited work.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Unresolved cited work
Reference 45
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T23:12:41.590319Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 8ca4e3d9-dcfb-4ad8-89d9-9c3e9f4c8277 · outbound
This paper cites an unresolved cited work.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Unresolved cited work
Reference 46
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T23:12:41.579668Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 4ef7cce6-d71a-4904-8380-2ed27a691e35 · outbound
This paper cites Sparse Gaussian processes revisited: Bayesian approaches to inducing-variable approximations.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Sparse Gaussian processes revisited: Bayesian approaches to inducing-variable approximations
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.567836Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation e726fb4a-1194-4706-9b1f-4be15f001ca1 · outbound
This paper cites an unresolved cited work.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Unresolved cited work
Reference 48
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T23:12:41.553495Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 3c7cce81-60a9-4f69-ab2b-8b1295c2b8ef · outbound
This paper cites an unresolved cited work.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Unresolved cited work
Reference 49
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T23:12:41.540060Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 62acfa1e-01d8-46b4-950a-3a4d8f39a379 · outbound
This paper cites OCO-2 Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files, retrospective processing v11.2r, 2017.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression OCO-2 Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files, retrospective processing v11.2r, 2017
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.527059Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 8b794620-ca20-4fe3-87ab-5ee0fe819c5c · outbound
This paper cites Stan modeling language users guide and reference manual, 2.35, 2024.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Stan modeling language users guide and reference manual, 2.35, 2024
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.514852Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 07cc1266-7c2c-458f-8ce0-400ea025e205 · outbound
This paper cites an unresolved cited work.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Unresolved cited work
Reference 52
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T23:12:41.502496Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 10c6d52d-14d3-45f6-91bd-9119ce3dfa59 · outbound
This paper cites Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J
Reference 53
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T23:12:41.380433Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 72b2d33b-442a-4249-8b0a-65f85cb5ac81 · outbound
This paper cites effectively independent.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression effectively independent
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T23:12:41.478683Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation db79c9b4-6e69-473a-a303-4c4f58e590a8 · outbound
This paper cites an unresolved cited work.
A gradient-based and determinant-free framework for fully Bayesian Gaussian process regression Unresolved cited work
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T23:12:41.177901Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.