Deep generative models for Bayesian inference on high-rate sensor data: applications in automotive radar and medical imaging
- DOI
- 10.1098/rsta.2024.0327
- Published
- 2025-06-19
- Container
- Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
- Publisher
- The Royal Society
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1098/rsta.2024.0327,
title = {Deep generative models for Bayesian inference on high-rate sensor data: applications in automotive radar and medical imaging},
author = {Tristan S. W. Stevens and Jeroen Overdevest and Oisín Nolan and Wessel L. van Nierop and Ruud J. G. van Sloun and Yonina C. Eldar},
year = {2025},
journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
doi = {10.1098/rsta.2024.0327},
url = {https://doi.org/10.1098/rsta.2024.0327}
}RIS
TY - JOUR TI - Deep generative models for Bayesian inference on high-rate sensor data: applications in automotive radar and medical imaging AU - Tristan S. W. Stevens AU - Jeroen Overdevest AU - Oisín Nolan AU - Wessel L. van Nierop AU - Ruud J. G. van Sloun AU - Yonina C. Eldar PY - 2025 JO - Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences DO - 10.1098/rsta.2024.0327 UR - https://doi.org/10.1098/rsta.2024.0327 ER -
APA
Stevens, T. S. W., Overdevest, J., Nolan, O., Nierop, W. L. V., Sloun, R. J. G. V., & Eldar, Y. C. (2025). Deep generative models for Bayesian inference on high-rate sensor data: applications in automotive radar and medical imaging. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. https://doi.org/10.1098/rsta.2024.0327
Source records
- crossref · retrieved 2026-09-25T04:44:49.974Z